{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "be72dc58",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Dropout\n",
    "from keras import optimizers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75c7ff14",
   "metadata": {},
   "source": [
    "## 数据导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0d50fb10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.1, 3.5, 1.4, 0.2],\n",
       "       [4.9, 3. , 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.3, 0.2],\n",
       "       [4.6, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.6, 1.4, 0.2],\n",
       "       [5.4, 3.9, 1.7, 0.4],\n",
       "       [4.6, 3.4, 1.4, 0.3],\n",
       "       [5. , 3.4, 1.5, 0.2],\n",
       "       [4.4, 2.9, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [5.4, 3.7, 1.5, 0.2],\n",
       "       [4.8, 3.4, 1.6, 0.2],\n",
       "       [4.8, 3. , 1.4, 0.1],\n",
       "       [4.3, 3. , 1.1, 0.1],\n",
       "       [5.8, 4. , 1.2, 0.2],\n",
       "       [5.7, 4.4, 1.5, 0.4],\n",
       "       [5.4, 3.9, 1.3, 0.4],\n",
       "       [5.1, 3.5, 1.4, 0.3],\n",
       "       [5.7, 3.8, 1.7, 0.3],\n",
       "       [5.1, 3.8, 1.5, 0.3],\n",
       "       [5.4, 3.4, 1.7, 0.2],\n",
       "       [5.1, 3.7, 1.5, 0.4],\n",
       "       [4.6, 3.6, 1. , 0.2],\n",
       "       [5.1, 3.3, 1.7, 0.5],\n",
       "       [4.8, 3.4, 1.9, 0.2],\n",
       "       [5. , 3. , 1.6, 0.2],\n",
       "       [5. , 3.4, 1.6, 0.4],\n",
       "       [5.2, 3.5, 1.5, 0.2],\n",
       "       [5.2, 3.4, 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.6, 0.2],\n",
       "       [4.8, 3.1, 1.6, 0.2],\n",
       "       [5.4, 3.4, 1.5, 0.4],\n",
       "       [5.2, 4.1, 1.5, 0.1],\n",
       "       [5.5, 4.2, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.2, 1.2, 0.2],\n",
       "       [5.5, 3.5, 1.3, 0.2],\n",
       "       [4.9, 3.6, 1.4, 0.1],\n",
       "       [4.4, 3. , 1.3, 0.2],\n",
       "       [5.1, 3.4, 1.5, 0.2],\n",
       "       [5. , 3.5, 1.3, 0.3],\n",
       "       [4.5, 2.3, 1.3, 0.3],\n",
       "       [4.4, 3.2, 1.3, 0.2],\n",
       "       [5. , 3.5, 1.6, 0.6],\n",
       "       [5.1, 3.8, 1.9, 0.4],\n",
       "       [4.8, 3. , 1.4, 0.3],\n",
       "       [5.1, 3.8, 1.6, 0.2],\n",
       "       [4.6, 3.2, 1.4, 0.2],\n",
       "       [5.3, 3.7, 1.5, 0.2],\n",
       "       [5. , 3.3, 1.4, 0.2],\n",
       "       [7. , 3.2, 4.7, 1.4],\n",
       "       [6.4, 3.2, 4.5, 1.5],\n",
       "       [6.9, 3.1, 4.9, 1.5],\n",
       "       [5.5, 2.3, 4. , 1.3],\n",
       "       [6.5, 2.8, 4.6, 1.5],\n",
       "       [5.7, 2.8, 4.5, 1.3],\n",
       "       [6.3, 3.3, 4.7, 1.6],\n",
       "       [4.9, 2.4, 3.3, 1. ],\n",
       "       [6.6, 2.9, 4.6, 1.3],\n",
       "       [5.2, 2.7, 3.9, 1.4],\n",
       "       [5. , 2. , 3.5, 1. ],\n",
       "       [5.9, 3. , 4.2, 1.5],\n",
       "       [6. , 2.2, 4. , 1. ],\n",
       "       [6.1, 2.9, 4.7, 1.4],\n",
       "       [5.6, 2.9, 3.6, 1.3],\n",
       "       [6.7, 3.1, 4.4, 1.4],\n",
       "       [5.6, 3. , 4.5, 1.5],\n",
       "       [5.8, 2.7, 4.1, 1. ],\n",
       "       [6.2, 2.2, 4.5, 1.5],\n",
       "       [5.6, 2.5, 3.9, 1.1],\n",
       "       [5.9, 3.2, 4.8, 1.8],\n",
       "       [6.1, 2.8, 4. , 1.3],\n",
       "       [6.3, 2.5, 4.9, 1.5],\n",
       "       [6.1, 2.8, 4.7, 1.2],\n",
       "       [6.4, 2.9, 4.3, 1.3],\n",
       "       [6.6, 3. , 4.4, 1.4],\n",
       "       [6.8, 2.8, 4.8, 1.4],\n",
       "       [6.7, 3. , 5. , 1.7],\n",
       "       [6. , 2.9, 4.5, 1.5],\n",
       "       [5.7, 2.6, 3.5, 1. ],\n",
       "       [5.5, 2.4, 3.8, 1.1],\n",
       "       [5.5, 2.4, 3.7, 1. ],\n",
       "       [5.8, 2.7, 3.9, 1.2],\n",
       "       [6. , 2.7, 5.1, 1.6],\n",
       "       [5.4, 3. , 4.5, 1.5],\n",
       "       [6. , 3.4, 4.5, 1.6],\n",
       "       [6.7, 3.1, 4.7, 1.5],\n",
       "       [6.3, 2.3, 4.4, 1.3],\n",
       "       [5.6, 3. , 4.1, 1.3],\n",
       "       [5.5, 2.5, 4. , 1.3],\n",
       "       [5.5, 2.6, 4.4, 1.2],\n",
       "       [6.1, 3. , 4.6, 1.4],\n",
       "       [5.8, 2.6, 4. , 1.2],\n",
       "       [5. , 2.3, 3.3, 1. ],\n",
       "       [5.6, 2.7, 4.2, 1.3],\n",
       "       [5.7, 3. , 4.2, 1.2],\n",
       "       [5.7, 2.9, 4.2, 1.3],\n",
       "       [6.2, 2.9, 4.3, 1.3],\n",
       "       [5.1, 2.5, 3. , 1.1],\n",
       "       [5.7, 2.8, 4.1, 1.3],\n",
       "       [6.3, 3.3, 6. , 2.5],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [7.1, 3. , 5.9, 2.1],\n",
       "       [6.3, 2.9, 5.6, 1.8],\n",
       "       [6.5, 3. , 5.8, 2.2],\n",
       "       [7.6, 3. , 6.6, 2.1],\n",
       "       [4.9, 2.5, 4.5, 1.7],\n",
       "       [7.3, 2.9, 6.3, 1.8],\n",
       "       [6.7, 2.5, 5.8, 1.8],\n",
       "       [7.2, 3.6, 6.1, 2.5],\n",
       "       [6.5, 3.2, 5.1, 2. ],\n",
       "       [6.4, 2.7, 5.3, 1.9],\n",
       "       [6.8, 3. , 5.5, 2.1],\n",
       "       [5.7, 2.5, 5. , 2. ],\n",
       "       [5.8, 2.8, 5.1, 2.4],\n",
       "       [6.4, 3.2, 5.3, 2.3],\n",
       "       [6.5, 3. , 5.5, 1.8],\n",
       "       [7.7, 3.8, 6.7, 2.2],\n",
       "       [7.7, 2.6, 6.9, 2.3],\n",
       "       [6. , 2.2, 5. , 1.5],\n",
       "       [6.9, 3.2, 5.7, 2.3],\n",
       "       [5.6, 2.8, 4.9, 2. ],\n",
       "       [7.7, 2.8, 6.7, 2. ],\n",
       "       [6.3, 2.7, 4.9, 1.8],\n",
       "       [6.7, 3.3, 5.7, 2.1],\n",
       "       [7.2, 3.2, 6. , 1.8],\n",
       "       [6.2, 2.8, 4.8, 1.8],\n",
       "       [6.1, 3. , 4.9, 1.8],\n",
       "       [6.4, 2.8, 5.6, 2.1],\n",
       "       [7.2, 3. , 5.8, 1.6],\n",
       "       [7.4, 2.8, 6.1, 1.9],\n",
       "       [7.9, 3.8, 6.4, 2. ],\n",
       "       [6.4, 2.8, 5.6, 2.2],\n",
       "       [6.3, 2.8, 5.1, 1.5],\n",
       "       [6.1, 2.6, 5.6, 1.4],\n",
       "       [7.7, 3. , 6.1, 2.3],\n",
       "       [6.3, 3.4, 5.6, 2.4],\n",
       "       [6.4, 3.1, 5.5, 1.8],\n",
       "       [6. , 3. , 4.8, 1.8],\n",
       "       [6.9, 3.1, 5.4, 2.1],\n",
       "       [6.7, 3.1, 5.6, 2.4],\n",
       "       [6.9, 3.1, 5.1, 2.3],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [6.8, 3.2, 5.9, 2.3],\n",
       "       [6.7, 3.3, 5.7, 2.5],\n",
       "       [6.7, 3. , 5.2, 2.3],\n",
       "       [6.3, 2.5, 5. , 1.9],\n",
       "       [6.5, 3. , 5.2, 2. ],\n",
       "       [6.2, 3.4, 5.4, 2.3],\n",
       "       [5.9, 3. , 5.1, 1.8]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#导入数据并预览\n",
    "iris_data = datasets.load_iris()\n",
    "input_data = iris_data.data\n",
    "input_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8575336c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "特征 ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n",
      "(150, 4)\n",
      "类: ['setosa' 'versicolor' 'virginica']\n",
      "(150,)\n"
     ]
    }
   ],
   "source": [
    "data = iris_data.data\n",
    "target = iris_data.target\n",
    "\n",
    "#数据\n",
    "print(\"特征\" ,iris_data.feature_names)\n",
    "print(data.shape)\n",
    "\n",
    "print(\"类:\" ,iris_data.target_names)\n",
    "print(target.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "777f6204",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    50\n",
       "1    50\n",
       "2    50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = pd.DataFrame(data)\n",
    "t = pd.DataFrame(target)\n",
    "t.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1dfc9667",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#数据简单可视化\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "#获取花卉两列特征值\n",
    "X = [x[0] for x in data]\n",
    "Y = [x[1] for x in data]\n",
    "\n",
    "plt.scatter(X[:50], Y[:50], color='red', marker='o', label='setosa')#前50个\n",
    "plt.scatter(X[50:100], Y[50:100], color='blue', marker='x', label='versicolor')#中间50个\n",
    "plt.scatter(X[100:], Y[100:], color='green', marker='+', label='Virginica')#后50个\n",
    "\n",
    "plt.legend(loc=2)#loc=1,2,3,4分别表示：\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bd8439e",
   "metadata": {},
   "source": [
    "## 数据标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "437b7fa4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-3.76591913e-01,  2.24266395e-01, -1.19462385e+00,\n",
       "        -5.06288142e-01],\n",
       "       [-4.77917092e-01, -2.90465511e-02, -1.19462385e+00,\n",
       "        -5.06288142e-01],\n",
       "       [-5.79242270e-01,  7.22786273e-02, -1.24528644e+00,\n",
       "        -5.06288142e-01],\n",
       "       [-6.29904859e-01,  2.16160381e-02, -1.14396126e+00,\n",
       "        -5.06288142e-01],\n",
       "       [-4.27254502e-01,  2.74928984e-01, -1.19462385e+00,\n",
       "        -5.06288142e-01],\n",
       "       [-2.24604145e-01,  4.26916752e-01, -1.04263609e+00,\n",
       "        -4.04962963e-01],\n",
       "       [-6.29904859e-01,  1.73603806e-01, -1.19462385e+00,\n",
       "        -4.55625552e-01],\n",
       "       [-4.27254502e-01,  1.73603806e-01, -1.14396126e+00,\n",
       "        -5.06288142e-01],\n",
       "       [-7.31230038e-01, -7.97091404e-02, -1.19462385e+00,\n",
       "        -5.06288142e-01],\n",
       "       [-4.77917092e-01,  2.16160381e-02, -1.14396126e+00,\n",
       "        -5.56950731e-01],\n",
       "       [-2.24604145e-01,  3.25591573e-01, -1.14396126e+00,\n",
       "        -5.06288142e-01],\n",
       "       [-5.28579681e-01,  1.73603806e-01, -1.09329868e+00,\n",
       "        -5.06288142e-01],\n",
       "       [-5.28579681e-01, -2.90465511e-02, -1.19462385e+00,\n",
       "        -5.56950731e-01],\n",
       "       [-7.81892627e-01, -2.90465511e-02, -1.34661162e+00,\n",
       "        -5.56950731e-01],\n",
       "       [-2.19537887e-02,  4.77579341e-01, -1.29594903e+00,\n",
       "        -5.06288142e-01],\n",
       "       [-7.26163779e-02,  6.80229698e-01, -1.14396126e+00,\n",
       "        -4.04962963e-01],\n",
       "       [-2.24604145e-01,  4.26916752e-01, -1.24528644e+00,\n",
       "        -4.04962963e-01],\n",
       "       [-3.76591913e-01,  2.24266395e-01, -1.19462385e+00,\n",
       "        -4.55625552e-01],\n",
       "       [-7.26163779e-02,  3.76254163e-01, -1.04263609e+00,\n",
       "        -4.55625552e-01],\n",
       "       [-3.76591913e-01,  3.76254163e-01, -1.14396126e+00,\n",
       "        -4.55625552e-01],\n",
       "       [-2.24604145e-01,  1.73603806e-01, -1.04263609e+00,\n",
       "        -5.06288142e-01],\n",
       "       [-3.76591913e-01,  3.25591573e-01, -1.14396126e+00,\n",
       "        -4.04962963e-01],\n",
       "       [-6.29904859e-01,  2.74928984e-01, -1.39727421e+00,\n",
       "        -5.06288142e-01],\n",
       "       [-3.76591913e-01,  1.22941216e-01, -1.04263609e+00,\n",
       "        -3.54300374e-01],\n",
       "       [-5.28579681e-01,  1.73603806e-01, -9.41310908e-01,\n",
       "        -5.06288142e-01],\n",
       "       [-4.27254502e-01, -2.90465511e-02, -1.09329868e+00,\n",
       "        -5.06288142e-01],\n",
       "       [-4.27254502e-01,  1.73603806e-01, -1.09329868e+00,\n",
       "        -4.04962963e-01],\n",
       "       [-3.25929324e-01,  2.24266395e-01, -1.14396126e+00,\n",
       "        -5.06288142e-01],\n",
       "       [-3.25929324e-01,  1.73603806e-01, -1.19462385e+00,\n",
       "        -5.06288142e-01],\n",
       "       [-5.79242270e-01,  7.22786273e-02, -1.09329868e+00,\n",
       "        -5.06288142e-01],\n",
       "       [-5.28579681e-01,  2.16160381e-02, -1.09329868e+00,\n",
       "        -5.06288142e-01],\n",
       "       [-2.24604145e-01,  1.73603806e-01, -1.14396126e+00,\n",
       "        -4.04962963e-01],\n",
       "       [-3.25929324e-01,  5.28241930e-01, -1.14396126e+00,\n",
       "        -5.56950731e-01],\n",
       "       [-1.73941556e-01,  5.78904519e-01, -1.19462385e+00,\n",
       "        -5.06288142e-01],\n",
       "       [-4.77917092e-01,  2.16160381e-02, -1.14396126e+00,\n",
       "        -5.06288142e-01],\n",
       "       [-4.27254502e-01,  7.22786273e-02, -1.29594903e+00,\n",
       "        -5.06288142e-01],\n",
       "       [-1.73941556e-01,  2.24266395e-01, -1.24528644e+00,\n",
       "        -5.06288142e-01],\n",
       "       [-4.77917092e-01,  2.74928984e-01, -1.19462385e+00,\n",
       "        -5.56950731e-01],\n",
       "       [-7.31230038e-01, -2.90465511e-02, -1.24528644e+00,\n",
       "        -5.06288142e-01],\n",
       "       [-3.76591913e-01,  1.73603806e-01, -1.14396126e+00,\n",
       "        -5.06288142e-01],\n",
       "       [-4.27254502e-01,  2.24266395e-01, -1.24528644e+00,\n",
       "        -4.55625552e-01],\n",
       "       [-6.80567448e-01, -3.83684676e-01, -1.24528644e+00,\n",
       "        -4.55625552e-01],\n",
       "       [-7.31230038e-01,  7.22786273e-02, -1.24528644e+00,\n",
       "        -5.06288142e-01],\n",
       "       [-4.27254502e-01,  2.24266395e-01, -1.09329868e+00,\n",
       "        -3.03637785e-01],\n",
       "       [-3.76591913e-01,  3.76254163e-01, -9.41310908e-01,\n",
       "        -4.04962963e-01],\n",
       "       [-5.28579681e-01, -2.90465511e-02, -1.19462385e+00,\n",
       "        -4.55625552e-01],\n",
       "       [-3.76591913e-01,  3.76254163e-01, -1.09329868e+00,\n",
       "        -5.06288142e-01],\n",
       "       [-6.29904859e-01,  7.22786273e-02, -1.19462385e+00,\n",
       "        -5.06288142e-01],\n",
       "       [-2.75266735e-01,  3.25591573e-01, -1.14396126e+00,\n",
       "        -5.06288142e-01],\n",
       "       [-4.27254502e-01,  1.22941216e-01, -1.19462385e+00,\n",
       "        -5.06288142e-01],\n",
       "       [ 5.85997282e-01,  7.22786273e-02,  4.77241590e-01,\n",
       "         1.01662929e-01],\n",
       "       [ 2.82021747e-01,  7.22786273e-02,  3.75916412e-01,\n",
       "         1.52325518e-01],\n",
       "       [ 5.35334693e-01,  2.16160381e-02,  5.78566769e-01,\n",
       "         1.52325518e-01],\n",
       "       [-1.73941556e-01, -3.83684676e-01,  1.22603466e-01,\n",
       "         5.10003398e-02],\n",
       "       [ 3.32684336e-01, -1.30371730e-01,  4.26579001e-01,\n",
       "         1.52325518e-01],\n",
       "       [-7.26163779e-02, -1.30371730e-01,  3.75916412e-01,\n",
       "         5.10003398e-02],\n",
       "       [ 2.31359157e-01,  1.22941216e-01,  4.77241590e-01,\n",
       "         2.02988107e-01],\n",
       "       [-4.77917092e-01, -3.33022086e-01, -2.32034659e-01,\n",
       "        -1.00987428e-01],\n",
       "       [ 3.83346925e-01, -7.97091404e-02,  4.26579001e-01,\n",
       "         5.10003398e-02],\n",
       "       [-3.25929324e-01, -1.81034319e-01,  7.19408767e-02,\n",
       "         1.01662929e-01],\n",
       "       [-4.27254502e-01, -5.35672443e-01, -1.30709480e-01,\n",
       "        -1.00987428e-01],\n",
       "       [ 2.87088006e-02, -2.90465511e-02,  2.23928644e-01,\n",
       "         1.52325518e-01],\n",
       "       [ 7.93713898e-02, -4.34347265e-01,  1.22603466e-01,\n",
       "        -1.00987428e-01],\n",
       "       [ 1.30033979e-01, -7.97091404e-02,  4.77241590e-01,\n",
       "         1.01662929e-01],\n",
       "       [-1.23278967e-01, -7.97091404e-02, -8.00468910e-02,\n",
       "         5.10003398e-02],\n",
       "       [ 4.34009514e-01,  2.16160381e-02,  3.25253823e-01,\n",
       "         1.01662929e-01],\n",
       "       [-1.23278967e-01, -2.90465511e-02,  3.75916412e-01,\n",
       "         1.52325518e-01],\n",
       "       [-2.19537887e-02, -1.81034319e-01,  1.73266055e-01,\n",
       "        -1.00987428e-01],\n",
       "       [ 1.80696568e-01, -4.34347265e-01,  3.75916412e-01,\n",
       "         1.52325518e-01],\n",
       "       [-1.23278967e-01, -2.82359497e-01,  7.19408767e-02,\n",
       "        -5.03248386e-02],\n",
       "       [ 2.87088006e-02,  7.22786273e-02,  5.27904180e-01,\n",
       "         3.04313286e-01],\n",
       "       [ 1.30033979e-01, -1.30371730e-01,  1.22603466e-01,\n",
       "         5.10003398e-02],\n",
       "       [ 2.31359157e-01, -2.82359497e-01,  5.78566769e-01,\n",
       "         1.52325518e-01],\n",
       "       [ 1.30033979e-01, -1.30371730e-01,  4.77241590e-01,\n",
       "         3.37750595e-04],\n",
       "       [ 2.82021747e-01, -7.97091404e-02,  2.74591234e-01,\n",
       "         5.10003398e-02],\n",
       "       [ 3.83346925e-01, -2.90465511e-02,  3.25253823e-01,\n",
       "         1.01662929e-01],\n",
       "       [ 4.84672103e-01, -1.30371730e-01,  5.27904180e-01,\n",
       "         1.01662929e-01],\n",
       "       [ 4.34009514e-01, -2.90465511e-02,  6.29229358e-01,\n",
       "         2.53650697e-01],\n",
       "       [ 7.93713898e-02, -7.97091404e-02,  3.75916412e-01,\n",
       "         1.52325518e-01],\n",
       "       [-7.26163779e-02, -2.31696908e-01, -1.30709480e-01,\n",
       "        -1.00987428e-01],\n",
       "       [-1.73941556e-01, -3.33022086e-01,  2.12782875e-02,\n",
       "        -5.03248386e-02],\n",
       "       [-1.73941556e-01, -3.33022086e-01, -2.93843017e-02,\n",
       "        -1.00987428e-01],\n",
       "       [-2.19537887e-02, -1.81034319e-01,  7.19408767e-02,\n",
       "         3.37750595e-04],\n",
       "       [ 7.93713898e-02, -1.81034319e-01,  6.79891947e-01,\n",
       "         2.02988107e-01],\n",
       "       [-2.24604145e-01, -2.90465511e-02,  3.75916412e-01,\n",
       "         1.52325518e-01],\n",
       "       [ 7.93713898e-02,  1.73603806e-01,  3.75916412e-01,\n",
       "         2.02988107e-01],\n",
       "       [ 4.34009514e-01,  2.16160381e-02,  4.77241590e-01,\n",
       "         1.52325518e-01],\n",
       "       [ 2.31359157e-01, -3.83684676e-01,  3.25253823e-01,\n",
       "         5.10003398e-02],\n",
       "       [-1.23278967e-01, -2.90465511e-02,  1.73266055e-01,\n",
       "         5.10003398e-02],\n",
       "       [-1.73941556e-01, -2.82359497e-01,  1.22603466e-01,\n",
       "         5.10003398e-02],\n",
       "       [-1.73941556e-01, -2.31696908e-01,  3.25253823e-01,\n",
       "         3.37750595e-04],\n",
       "       [ 1.30033979e-01, -2.90465511e-02,  4.26579001e-01,\n",
       "         1.01662929e-01],\n",
       "       [-2.19537887e-02, -2.31696908e-01,  1.22603466e-01,\n",
       "         3.37750595e-04],\n",
       "       [-4.27254502e-01, -3.83684676e-01, -2.32034659e-01,\n",
       "        -1.00987428e-01],\n",
       "       [-1.23278967e-01, -1.81034319e-01,  2.23928644e-01,\n",
       "         5.10003398e-02],\n",
       "       [-7.26163779e-02, -2.90465511e-02,  2.23928644e-01,\n",
       "         3.37750595e-04],\n",
       "       [-7.26163779e-02, -7.97091404e-02,  2.23928644e-01,\n",
       "         5.10003398e-02],\n",
       "       [ 1.80696568e-01, -7.97091404e-02,  2.74591234e-01,\n",
       "         5.10003398e-02],\n",
       "       [-3.76591913e-01, -2.82359497e-01, -3.84022426e-01,\n",
       "        -5.03248386e-02],\n",
       "       [-7.26163779e-02, -1.30371730e-01,  1.73266055e-01,\n",
       "         5.10003398e-02],\n",
       "       [ 2.31359157e-01,  1.22941216e-01,  1.13585525e+00,\n",
       "         6.58951410e-01],\n",
       "       [-2.19537887e-02, -1.81034319e-01,  6.79891947e-01,\n",
       "         3.54975875e-01],\n",
       "       [ 6.36659871e-01, -2.90465511e-02,  1.08519266e+00,\n",
       "         4.56301053e-01],\n",
       "       [ 2.31359157e-01, -7.97091404e-02,  9.33204893e-01,\n",
       "         3.04313286e-01],\n",
       "       [ 3.32684336e-01, -2.90465511e-02,  1.03453007e+00,\n",
       "         5.06963643e-01],\n",
       "       [ 8.89972817e-01, -2.90465511e-02,  1.43983079e+00,\n",
       "         4.56301053e-01],\n",
       "       [-4.77917092e-01, -2.82359497e-01,  3.75916412e-01,\n",
       "         2.53650697e-01],\n",
       "       [ 7.37985049e-01, -7.97091404e-02,  1.28784302e+00,\n",
       "         3.04313286e-01],\n",
       "       [ 4.34009514e-01, -2.82359497e-01,  1.03453007e+00,\n",
       "         3.04313286e-01],\n",
       "       [ 6.87322460e-01,  2.74928984e-01,  1.18651784e+00,\n",
       "         6.58951410e-01],\n",
       "       [ 3.32684336e-01,  7.22786273e-02,  6.79891947e-01,\n",
       "         4.05638464e-01],\n",
       "       [ 2.82021747e-01, -1.81034319e-01,  7.81217126e-01,\n",
       "         3.54975875e-01],\n",
       "       [ 4.84672103e-01, -2.90465511e-02,  8.82542304e-01,\n",
       "         4.56301053e-01],\n",
       "       [-7.26163779e-02, -2.82359497e-01,  6.29229358e-01,\n",
       "         4.05638464e-01],\n",
       "       [-2.19537887e-02, -1.30371730e-01,  6.79891947e-01,\n",
       "         6.08288821e-01],\n",
       "       [ 2.82021747e-01,  7.22786273e-02,  7.81217126e-01,\n",
       "         5.57626232e-01],\n",
       "       [ 3.32684336e-01, -2.90465511e-02,  8.82542304e-01,\n",
       "         3.04313286e-01],\n",
       "       [ 9.40635406e-01,  3.76254163e-01,  1.49049337e+00,\n",
       "         5.06963643e-01],\n",
       "       [ 9.40635406e-01, -2.31696908e-01,  1.59181855e+00,\n",
       "         5.57626232e-01],\n",
       "       [ 7.93713898e-02, -4.34347265e-01,  6.29229358e-01,\n",
       "         1.52325518e-01],\n",
       "       [ 5.35334693e-01,  7.22786273e-02,  9.83867482e-01,\n",
       "         5.57626232e-01],\n",
       "       [-1.23278967e-01, -1.30371730e-01,  5.78566769e-01,\n",
       "         4.05638464e-01],\n",
       "       [ 9.40635406e-01, -1.30371730e-01,  1.49049337e+00,\n",
       "         4.05638464e-01],\n",
       "       [ 2.31359157e-01, -1.81034319e-01,  5.78566769e-01,\n",
       "         3.04313286e-01],\n",
       "       [ 4.34009514e-01,  1.22941216e-01,  9.83867482e-01,\n",
       "         4.56301053e-01],\n",
       "       [ 6.87322460e-01,  7.22786273e-02,  1.13585525e+00,\n",
       "         3.04313286e-01],\n",
       "       [ 1.80696568e-01, -1.30371730e-01,  5.27904180e-01,\n",
       "         3.04313286e-01],\n",
       "       [ 1.30033979e-01, -2.90465511e-02,  5.78566769e-01,\n",
       "         3.04313286e-01],\n",
       "       [ 2.82021747e-01, -1.30371730e-01,  9.33204893e-01,\n",
       "         4.56301053e-01],\n",
       "       [ 6.87322460e-01, -2.90465511e-02,  1.03453007e+00,\n",
       "         2.02988107e-01],\n",
       "       [ 7.88647639e-01, -1.30371730e-01,  1.18651784e+00,\n",
       "         3.54975875e-01],\n",
       "       [ 1.04196058e+00,  3.76254163e-01,  1.33850561e+00,\n",
       "         4.05638464e-01],\n",
       "       [ 2.82021747e-01, -1.30371730e-01,  9.33204893e-01,\n",
       "         5.06963643e-01],\n",
       "       [ 2.31359157e-01, -1.30371730e-01,  6.79891947e-01,\n",
       "         1.52325518e-01],\n",
       "       [ 1.30033979e-01, -2.31696908e-01,  9.33204893e-01,\n",
       "         1.01662929e-01],\n",
       "       [ 9.40635406e-01, -2.90465511e-02,  1.18651784e+00,\n",
       "         5.57626232e-01],\n",
       "       [ 2.31359157e-01,  1.73603806e-01,  9.33204893e-01,\n",
       "         6.08288821e-01],\n",
       "       [ 2.82021747e-01,  2.16160381e-02,  8.82542304e-01,\n",
       "         3.04313286e-01],\n",
       "       [ 7.93713898e-02, -2.90465511e-02,  5.27904180e-01,\n",
       "         3.04313286e-01],\n",
       "       [ 5.35334693e-01,  2.16160381e-02,  8.31879715e-01,\n",
       "         4.56301053e-01],\n",
       "       [ 4.34009514e-01,  2.16160381e-02,  9.33204893e-01,\n",
       "         6.08288821e-01],\n",
       "       [ 5.35334693e-01,  2.16160381e-02,  6.79891947e-01,\n",
       "         5.57626232e-01],\n",
       "       [-2.19537887e-02, -1.81034319e-01,  6.79891947e-01,\n",
       "         3.54975875e-01],\n",
       "       [ 4.84672103e-01,  7.22786273e-02,  1.08519266e+00,\n",
       "         5.57626232e-01],\n",
       "       [ 4.34009514e-01,  1.22941216e-01,  9.83867482e-01,\n",
       "         6.58951410e-01],\n",
       "       [ 4.34009514e-01, -2.90465511e-02,  7.30554536e-01,\n",
       "         5.57626232e-01],\n",
       "       [ 2.31359157e-01, -2.82359497e-01,  6.29229358e-01,\n",
       "         3.54975875e-01],\n",
       "       [ 3.32684336e-01, -2.90465511e-02,  7.30554536e-01,\n",
       "         4.05638464e-01],\n",
       "       [ 1.80696568e-01,  1.73603806e-01,  8.31879715e-01,\n",
       "         5.57626232e-01],\n",
       "       [ 2.87088006e-02, -2.90465511e-02,  6.79891947e-01,\n",
       "         3.04313286e-01]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对数据进行标准化处理\n",
    "ave_input = np.average(input_data,axis=0)\n",
    "std_input = np.std(input_data)\n",
    "input_data = (input_data-ave_input)/std_input\n",
    "input_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3384a610",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# --将正确答案转换为独热编码格式--\n",
    "correct = iris_data.target\n",
    "n_data = len(correct) #样本数量\n",
    "correct_data= np.zeros((n_data,3))\n",
    "for i in range(n_data):\n",
    "    correct_data[i,correct[i]] = 1.0\n",
    "correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b1f0a22e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 划分训练集和测试集\n",
    "index = np.arange(n_data)\n",
    "index_train = index [index%2==0]\n",
    "index_test = index [index%2!=0]\n",
    "x_train = input_data[index_train,:]\n",
    "x_test = input_data[index_test,:]\n",
    "y_train = correct_data[index_train,:]\n",
    "y_test = correct_data[index_test,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91b3f646",
   "metadata": {},
   "source": [
    "## BP模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c1b1906d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 63 samples, validate on 12 samples\n",
      "Epoch 1/1000\n",
      "63/63 [==============================] - 0s 6ms/step - loss: 1.3437 - accuracy: 0.2540 - val_loss: 0.9359 - val_accuracy: 1.0000\n",
      "Epoch 2/1000\n",
      "63/63 [==============================] - 0s 200us/step - loss: 1.0925 - accuracy: 0.3651 - val_loss: 1.1784 - val_accuracy: 0.0000e+00\n",
      "Epoch 3/1000\n",
      "63/63 [==============================] - 0s 232us/step - loss: 0.9443 - accuracy: 0.5238 - val_loss: 1.2792 - val_accuracy: 0.0000e+00\n",
      "Epoch 4/1000\n",
      "63/63 [==============================] - 0s 286us/step - loss: 0.7760 - accuracy: 0.7143 - val_loss: 1.1028 - val_accuracy: 0.0000e+00\n",
      "Epoch 5/1000\n",
      "63/63 [==============================] - 0s 343us/step - loss: 0.6720 - accuracy: 0.7619 - val_loss: 0.7769 - val_accuracy: 0.4167\n",
      "Epoch 6/1000\n",
      "63/63 [==============================] - 0s 288us/step - loss: 0.5677 - accuracy: 0.7937 - val_loss: 0.9347 - val_accuracy: 0.0000e+00\n",
      "Epoch 7/1000\n",
      "63/63 [==============================] - 0s 273us/step - loss: 0.5246 - accuracy: 0.7619 - val_loss: 1.0054 - val_accuracy: 0.0000e+00\n",
      "Epoch 8/1000\n",
      "63/63 [==============================] - 0s 369us/step - loss: 0.4693 - accuracy: 0.7778 - val_loss: 0.8755 - val_accuracy: 0.0000e+00\n",
      "Epoch 9/1000\n",
      "63/63 [==============================] - 0s 292us/step - loss: 0.4144 - accuracy: 0.7937 - val_loss: 0.9458 - val_accuracy: 0.0000e+00\n",
      "Epoch 10/1000\n",
      "63/63 [==============================] - 0s 247us/step - loss: 0.4475 - accuracy: 0.7778 - val_loss: 0.9392 - val_accuracy: 0.0000e+00\n",
      "Epoch 11/1000\n",
      "63/63 [==============================] - 0s 321us/step - loss: 0.4398 - accuracy: 0.7778 - val_loss: 0.7777 - val_accuracy: 0.0000e+00\n",
      "Epoch 12/1000\n",
      "63/63 [==============================] - 0s 224us/step - loss: 0.4319 - accuracy: 0.8095 - val_loss: 0.9320 - val_accuracy: 0.0000e+00\n",
      "Epoch 13/1000\n",
      "63/63 [==============================] - 0s 314us/step - loss: 0.4184 - accuracy: 0.8095 - val_loss: 1.0005 - val_accuracy: 0.0000e+00\n",
      "Epoch 14/1000\n",
      "63/63 [==============================] - 0s 247us/step - loss: 0.3911 - accuracy: 0.7937 - val_loss: 0.9071 - val_accuracy: 0.0000e+00\n",
      "Epoch 15/1000\n",
      "63/63 [==============================] - 0s 233us/step - loss: 0.3994 - accuracy: 0.7778 - val_loss: 0.9184 - val_accuracy: 0.0000e+00\n",
      "Epoch 16/1000\n",
      "63/63 [==============================] - 0s 281us/step - loss: 0.4033 - accuracy: 0.7778 - val_loss: 0.8662 - val_accuracy: 0.0000e+00\n",
      "Epoch 17/1000\n",
      "63/63 [==============================] - 0s 253us/step - loss: 0.3475 - accuracy: 0.8095 - val_loss: 0.9407 - val_accuracy: 0.0000e+00\n",
      "Epoch 18/1000\n",
      "63/63 [==============================] - 0s 255us/step - loss: 0.4055 - accuracy: 0.7778 - val_loss: 0.7114 - val_accuracy: 0.5000\n",
      "Epoch 19/1000\n",
      "63/63 [==============================] - 0s 256us/step - loss: 0.3505 - accuracy: 0.8095 - val_loss: 0.7313 - val_accuracy: 0.4167\n",
      "Epoch 20/1000\n",
      "63/63 [==============================] - 0s 272us/step - loss: 0.3765 - accuracy: 0.8730 - val_loss: 0.7424 - val_accuracy: 0.3333\n",
      "Epoch 21/1000\n",
      "63/63 [==============================] - 0s 244us/step - loss: 0.4020 - accuracy: 0.8254 - val_loss: 0.8716 - val_accuracy: 0.0000e+00\n",
      "Epoch 22/1000\n",
      "63/63 [==============================] - 0s 366us/step - loss: 0.3642 - accuracy: 0.7937 - val_loss: 0.7149 - val_accuracy: 0.5000\n",
      "Epoch 23/1000\n",
      "63/63 [==============================] - 0s 191us/step - loss: 0.3393 - accuracy: 0.7937 - val_loss: 0.7540 - val_accuracy: 0.4167\n",
      "Epoch 24/1000\n",
      "63/63 [==============================] - 0s 279us/step - loss: 0.3527 - accuracy: 0.8413 - val_loss: 0.6372 - val_accuracy: 0.8333\n",
      "Epoch 25/1000\n",
      "63/63 [==============================] - 0s 167us/step - loss: 0.3059 - accuracy: 0.9048 - val_loss: 0.6271 - val_accuracy: 0.8333\n",
      "Epoch 26/1000\n",
      "63/63 [==============================] - 0s 258us/step - loss: 0.3614 - accuracy: 0.8413 - val_loss: 0.7057 - val_accuracy: 0.5000\n",
      "Epoch 27/1000\n",
      "63/63 [==============================] - 0s 183us/step - loss: 0.3498 - accuracy: 0.9048 - val_loss: 0.6570 - val_accuracy: 0.6667\n",
      "Epoch 28/1000\n",
      "63/63 [==============================] - 0s 271us/step - loss: 0.2716 - accuracy: 0.9365 - val_loss: 0.6319 - val_accuracy: 0.7500\n",
      "Epoch 29/1000\n",
      "63/63 [==============================] - 0s 195us/step - loss: 0.2456 - accuracy: 0.9683 - val_loss: 0.8267 - val_accuracy: 0.4167\n",
      "Epoch 30/1000\n",
      "63/63 [==============================] - 0s 221us/step - loss: 0.2746 - accuracy: 0.9048 - val_loss: 0.6377 - val_accuracy: 0.6667\n",
      "Epoch 31/1000\n",
      "63/63 [==============================] - 0s 195us/step - loss: 0.2921 - accuracy: 0.9048 - val_loss: 0.5021 - val_accuracy: 0.8333\n",
      "Epoch 32/1000\n",
      "63/63 [==============================] - 0s 207us/step - loss: 0.2619 - accuracy: 0.9206 - val_loss: 0.5261 - val_accuracy: 0.8333\n",
      "Epoch 33/1000\n",
      "63/63 [==============================] - 0s 240us/step - loss: 0.2366 - accuracy: 0.9365 - val_loss: 0.4670 - val_accuracy: 0.8333\n",
      "Epoch 34/1000\n",
      "63/63 [==============================] - 0s 222us/step - loss: 0.2360 - accuracy: 0.9365 - val_loss: 0.4432 - val_accuracy: 0.9167\n",
      "Epoch 35/1000\n",
      "63/63 [==============================] - 0s 257us/step - loss: 0.2415 - accuracy: 0.9524 - val_loss: 0.5077 - val_accuracy: 0.8333\n",
      "Epoch 36/1000\n",
      "63/63 [==============================] - 0s 247us/step - loss: 0.2171 - accuracy: 0.9206 - val_loss: 0.3244 - val_accuracy: 1.0000\n",
      "Epoch 37/1000\n",
      "63/63 [==============================] - 0s 187us/step - loss: 0.2073 - accuracy: 0.9524 - val_loss: 0.3493 - val_accuracy: 0.9167\n",
      "Epoch 38/1000\n",
      "63/63 [==============================] - 0s 248us/step - loss: 0.1663 - accuracy: 0.9683 - val_loss: 0.3437 - val_accuracy: 0.9167\n",
      "Epoch 39/1000\n",
      "63/63 [==============================] - 0s 257us/step - loss: 0.1526 - accuracy: 0.9841 - val_loss: 0.3386 - val_accuracy: 0.9167\n",
      "Epoch 40/1000\n",
      "63/63 [==============================] - 0s 399us/step - loss: 0.1755 - accuracy: 0.9841 - val_loss: 0.3511 - val_accuracy: 0.9167\n",
      "Epoch 41/1000\n",
      "63/63 [==============================] - 0s 237us/step - loss: 0.2159 - accuracy: 0.9365 - val_loss: 0.3399 - val_accuracy: 0.9167\n",
      "Epoch 42/1000\n",
      "63/63 [==============================] - 0s 247us/step - loss: 0.2450 - accuracy: 0.9206 - val_loss: 0.3511 - val_accuracy: 0.9167\n",
      "Epoch 43/1000\n",
      "63/63 [==============================] - 0s 203us/step - loss: 0.1770 - accuracy: 0.9683 - val_loss: 0.3424 - val_accuracy: 0.9167\n",
      "Epoch 44/1000\n",
      "63/63 [==============================] - 0s 296us/step - loss: 0.1395 - accuracy: 0.9841 - val_loss: 0.3451 - val_accuracy: 0.8333\n",
      "Epoch 45/1000\n",
      "63/63 [==============================] - 0s 199us/step - loss: 0.1608 - accuracy: 0.9524 - val_loss: 0.3299 - val_accuracy: 0.9167\n",
      "Epoch 46/1000\n",
      "63/63 [==============================] - 0s 273us/step - loss: 0.2142 - accuracy: 0.9048 - val_loss: 0.2944 - val_accuracy: 0.9167\n",
      "Epoch 47/1000\n",
      "63/63 [==============================] - 0s 209us/step - loss: 0.1385 - accuracy: 0.9683 - val_loss: 0.2296 - val_accuracy: 1.0000\n",
      "Epoch 48/1000\n",
      "63/63 [==============================] - 0s 251us/step - loss: 0.1072 - accuracy: 0.9841 - val_loss: 0.3336 - val_accuracy: 0.8333\n",
      "Epoch 49/1000\n",
      "63/63 [==============================] - 0s 199us/step - loss: 0.2064 - accuracy: 0.9206 - val_loss: 0.1891 - val_accuracy: 1.0000\n",
      "Epoch 50/1000\n",
      "63/63 [==============================] - 0s 283us/step - loss: 0.1481 - accuracy: 0.9841 - val_loss: 0.1809 - val_accuracy: 1.0000\n",
      "Epoch 51/1000\n",
      "63/63 [==============================] - 0s 160us/step - loss: 0.1703 - accuracy: 0.9524 - val_loss: 0.1519 - val_accuracy: 1.0000\n",
      "Epoch 52/1000\n",
      "63/63 [==============================] - 0s 228us/step - loss: 0.1066 - accuracy: 0.9841 - val_loss: 0.1891 - val_accuracy: 1.0000\n",
      "Epoch 53/1000\n",
      "63/63 [==============================] - 0s 224us/step - loss: 0.1870 - accuracy: 0.9048 - val_loss: 0.1921 - val_accuracy: 1.0000\n",
      "Epoch 54/1000\n",
      "63/63 [==============================] - 0s 208us/step - loss: 0.1178 - accuracy: 0.9683 - val_loss: 0.2429 - val_accuracy: 0.9167\n",
      "Epoch 55/1000\n",
      "63/63 [==============================] - 0s 255us/step - loss: 0.1275 - accuracy: 0.9841 - val_loss: 0.2305 - val_accuracy: 0.9167\n",
      "Epoch 56/1000\n",
      "63/63 [==============================] - 0s 255us/step - loss: 0.1619 - accuracy: 0.9365 - val_loss: 0.1415 - val_accuracy: 1.0000\n",
      "Epoch 57/1000\n",
      "63/63 [==============================] - 0s 168us/step - loss: 0.1168 - accuracy: 0.9683 - val_loss: 0.2210 - val_accuracy: 0.9167\n",
      "Epoch 58/1000\n",
      "63/63 [==============================] - 0s 199us/step - loss: 0.1232 - accuracy: 0.9683 - val_loss: 0.2029 - val_accuracy: 1.0000\n",
      "Epoch 59/1000\n",
      "63/63 [==============================] - 0s 246us/step - loss: 0.1942 - accuracy: 0.9365 - val_loss: 0.1860 - val_accuracy: 1.0000\n",
      "Epoch 60/1000\n",
      "63/63 [==============================] - 0s 176us/step - loss: 0.1144 - accuracy: 0.9524 - val_loss: 0.1468 - val_accuracy: 1.0000\n",
      "Epoch 61/1000\n",
      "63/63 [==============================] - 0s 255us/step - loss: 0.0857 - accuracy: 0.9841 - val_loss: 0.1989 - val_accuracy: 1.0000\n",
      "Epoch 62/1000\n",
      "63/63 [==============================] - 0s 183us/step - loss: 0.0989 - accuracy: 0.9683 - val_loss: 0.1916 - val_accuracy: 1.0000\n",
      "Epoch 63/1000\n",
      "63/63 [==============================] - 0s 290us/step - loss: 0.0993 - accuracy: 0.9683 - val_loss: 0.2474 - val_accuracy: 0.9167\n",
      "Epoch 64/1000\n",
      "63/63 [==============================] - 0s 184us/step - loss: 0.1066 - accuracy: 0.9524 - val_loss: 0.1697 - val_accuracy: 1.0000\n",
      "Epoch 65/1000\n",
      "63/63 [==============================] - 0s 257us/step - loss: 0.1253 - accuracy: 0.9524 - val_loss: 0.1459 - val_accuracy: 1.0000\n",
      "Epoch 66/1000\n",
      "63/63 [==============================] - 0s 184us/step - loss: 0.1177 - accuracy: 0.9683 - val_loss: 0.2065 - val_accuracy: 0.9167\n",
      "Epoch 67/1000\n",
      "63/63 [==============================] - 0s 270us/step - loss: 0.0958 - accuracy: 0.9524 - val_loss: 0.2293 - val_accuracy: 0.9167\n",
      "Epoch 68/1000\n",
      "63/63 [==============================] - 0s 210us/step - loss: 0.1217 - accuracy: 0.9683 - val_loss: 0.1848 - val_accuracy: 1.0000\n",
      "Epoch 69/1000\n",
      "63/63 [==============================] - 0s 305us/step - loss: 0.0886 - accuracy: 0.9683 - val_loss: 0.2305 - val_accuracy: 0.9167\n",
      "Epoch 70/1000\n",
      "63/63 [==============================] - 0s 189us/step - loss: 0.1120 - accuracy: 0.9365 - val_loss: 0.2345 - val_accuracy: 0.9167\n",
      "Epoch 71/1000\n",
      "63/63 [==============================] - 0s 264us/step - loss: 0.0892 - accuracy: 0.9841 - val_loss: 0.3170 - val_accuracy: 0.8333\n",
      "Epoch 72/1000\n",
      "63/63 [==============================] - 0s 176us/step - loss: 0.0654 - accuracy: 0.9841 - val_loss: 0.3149 - val_accuracy: 0.8333\n",
      "Epoch 73/1000\n",
      "63/63 [==============================] - 0s 224us/step - loss: 0.0731 - accuracy: 0.9683 - val_loss: 0.1807 - val_accuracy: 0.9167\n",
      "Epoch 74/1000\n",
      "63/63 [==============================] - 0s 206us/step - loss: 0.0609 - accuracy: 1.0000 - val_loss: 0.2022 - val_accuracy: 0.9167\n",
      "Epoch 75/1000\n",
      "63/63 [==============================] - 0s 208us/step - loss: 0.0943 - accuracy: 0.9683 - val_loss: 0.2440 - val_accuracy: 0.8333\n",
      "Epoch 76/1000\n",
      "63/63 [==============================] - 0s 279us/step - loss: 0.0893 - accuracy: 0.9841 - val_loss: 0.3613 - val_accuracy: 0.7500\n",
      "Epoch 77/1000\n",
      "63/63 [==============================] - 0s 378us/step - loss: 0.0647 - accuracy: 0.9841 - val_loss: 0.2091 - val_accuracy: 0.9167\n",
      "Epoch 78/1000\n",
      "63/63 [==============================] - 0s 255us/step - loss: 0.0865 - accuracy: 0.9524 - val_loss: 0.1452 - val_accuracy: 1.0000\n",
      "Epoch 79/1000\n",
      "63/63 [==============================] - 0s 267us/step - loss: 0.1517 - accuracy: 0.9365 - val_loss: 0.1394 - val_accuracy: 1.0000\n",
      "Epoch 80/1000\n",
      "63/63 [==============================] - 0s 184us/step - loss: 0.0734 - accuracy: 0.9683 - val_loss: 0.2054 - val_accuracy: 0.9167\n",
      "Epoch 81/1000\n",
      "63/63 [==============================] - 0s 267us/step - loss: 0.0749 - accuracy: 0.9683 - val_loss: 0.1523 - val_accuracy: 1.0000\n",
      "Epoch 82/1000\n",
      "63/63 [==============================] - 0s 191us/step - loss: 0.0581 - accuracy: 0.9841 - val_loss: 0.2560 - val_accuracy: 0.8333\n",
      "Epoch 83/1000\n",
      "63/63 [==============================] - 0s 258us/step - loss: 0.1080 - accuracy: 0.9524 - val_loss: 0.1301 - val_accuracy: 1.0000\n",
      "Epoch 84/1000\n",
      "63/63 [==============================] - 0s 199us/step - loss: 0.0907 - accuracy: 0.9683 - val_loss: 0.1421 - val_accuracy: 1.0000\n",
      "Epoch 85/1000\n",
      "63/63 [==============================] - 0s 231us/step - loss: 0.0733 - accuracy: 0.9841 - val_loss: 0.1766 - val_accuracy: 0.9167\n",
      "Epoch 86/1000\n",
      "63/63 [==============================] - 0s 157us/step - loss: 0.0748 - accuracy: 0.9683 - val_loss: 0.2078 - val_accuracy: 0.8333\n",
      "Epoch 87/1000\n",
      "63/63 [==============================] - 0s 263us/step - loss: 0.0728 - accuracy: 0.9841 - val_loss: 0.1702 - val_accuracy: 0.9167\n",
      "Epoch 88/1000\n",
      "63/63 [==============================] - 0s 202us/step - loss: 0.1237 - accuracy: 0.9365 - val_loss: 0.1432 - val_accuracy: 1.0000\n",
      "Epoch 89/1000\n",
      "63/63 [==============================] - 0s 256us/step - loss: 0.0419 - accuracy: 1.0000 - val_loss: 0.1492 - val_accuracy: 1.0000\n",
      "Epoch 90/1000\n",
      "63/63 [==============================] - 0s 168us/step - loss: 0.0809 - accuracy: 0.9683 - val_loss: 0.1113 - val_accuracy: 1.0000\n",
      "Epoch 91/1000\n",
      "63/63 [==============================] - 0s 272us/step - loss: 0.0521 - accuracy: 0.9841 - val_loss: 0.1740 - val_accuracy: 0.9167\n",
      "Epoch 92/1000\n",
      "63/63 [==============================] - 0s 187us/step - loss: 0.0653 - accuracy: 0.9841 - val_loss: 0.2324 - val_accuracy: 0.8333\n",
      "Epoch 93/1000\n",
      "63/63 [==============================] - 0s 258us/step - loss: 0.0926 - accuracy: 0.9524 - val_loss: 0.1860 - val_accuracy: 0.9167\n",
      "Epoch 94/1000\n",
      "63/63 [==============================] - 0s 197us/step - loss: 0.0429 - accuracy: 1.0000 - val_loss: 0.2587 - val_accuracy: 0.8333\n",
      "Epoch 95/1000\n",
      "63/63 [==============================] - 0s 272us/step - loss: 0.0710 - accuracy: 0.9841 - val_loss: 0.2709 - val_accuracy: 0.8333\n",
      "Epoch 96/1000\n",
      "63/63 [==============================] - 0s 219us/step - loss: 0.1008 - accuracy: 0.9683 - val_loss: 0.1588 - val_accuracy: 0.9167\n",
      "Epoch 97/1000\n",
      "63/63 [==============================] - 0s 271us/step - loss: 0.0479 - accuracy: 1.0000 - val_loss: 0.2079 - val_accuracy: 0.8333\n",
      "Epoch 98/1000\n",
      "63/63 [==============================] - 0s 152us/step - loss: 0.0901 - accuracy: 0.9841 - val_loss: 0.1435 - val_accuracy: 1.0000\n",
      "Epoch 99/1000\n",
      "63/63 [==============================] - 0s 178us/step - loss: 0.0457 - accuracy: 1.0000 - val_loss: 0.1393 - val_accuracy: 1.0000\n",
      "Epoch 100/1000\n",
      "63/63 [==============================] - 0s 256us/step - loss: 0.0745 - accuracy: 0.9841 - val_loss: 0.1167 - val_accuracy: 1.0000\n",
      "Epoch 101/1000\n",
      "63/63 [==============================] - 0s 164us/step - loss: 0.0469 - accuracy: 0.9841 - val_loss: 0.1691 - val_accuracy: 0.9167\n",
      "Epoch 102/1000\n",
      "63/63 [==============================] - 0s 257us/step - loss: 0.0410 - accuracy: 0.9841 - val_loss: 0.1613 - val_accuracy: 0.9167\n",
      "Epoch 103/1000\n",
      "63/63 [==============================] - 0s 184us/step - loss: 0.0768 - accuracy: 0.9841 - val_loss: 0.1471 - val_accuracy: 0.9167\n",
      "Epoch 104/1000\n",
      "63/63 [==============================] - 0s 247us/step - loss: 0.0955 - accuracy: 0.9683 - val_loss: 0.1058 - val_accuracy: 1.0000\n",
      "Epoch 105/1000\n",
      "63/63 [==============================] - 0s 201us/step - loss: 0.0710 - accuracy: 0.9683 - val_loss: 0.1734 - val_accuracy: 0.9167\n",
      "Epoch 106/1000\n",
      "63/63 [==============================] - 0s 231us/step - loss: 0.1155 - accuracy: 0.9524 - val_loss: 0.0716 - val_accuracy: 1.0000\n",
      "Epoch 107/1000\n",
      "63/63 [==============================] - 0s 202us/step - loss: 0.0898 - accuracy: 0.9683 - val_loss: 0.1255 - val_accuracy: 1.0000\n",
      "Epoch 108/1000\n",
      "63/63 [==============================] - 0s 210us/step - loss: 0.0761 - accuracy: 0.9524 - val_loss: 0.1944 - val_accuracy: 0.8333\n",
      "Epoch 109/1000\n",
      "63/63 [==============================] - 0s 264us/step - loss: 0.0575 - accuracy: 0.9841 - val_loss: 0.2148 - val_accuracy: 0.8333\n",
      "Epoch 110/1000\n",
      "63/63 [==============================] - 0s 233us/step - loss: 0.0731 - accuracy: 0.9524 - val_loss: 0.1658 - val_accuracy: 0.9167\n",
      "Epoch 111/1000\n",
      "63/63 [==============================] - 0s 167us/step - loss: 0.0590 - accuracy: 0.9841 - val_loss: 0.1767 - val_accuracy: 0.9167\n",
      "Epoch 112/1000\n",
      "63/63 [==============================] - 0s 208us/step - loss: 0.0841 - accuracy: 0.9524 - val_loss: 0.1459 - val_accuracy: 0.9167\n",
      "Epoch 113/1000\n",
      "63/63 [==============================] - 0s 236us/step - loss: 0.0619 - accuracy: 0.9841 - val_loss: 0.2172 - val_accuracy: 0.8333\n",
      "Epoch 114/1000\n",
      "63/63 [==============================] - 0s 165us/step - loss: 0.0280 - accuracy: 1.0000 - val_loss: 0.2404 - val_accuracy: 0.8333\n",
      "Epoch 115/1000\n",
      "63/63 [==============================] - 0s 266us/step - loss: 0.0542 - accuracy: 0.9841 - val_loss: 0.2293 - val_accuracy: 0.8333\n",
      "Epoch 116/1000\n",
      "63/63 [==============================] - 0s 151us/step - loss: 0.0722 - accuracy: 0.9683 - val_loss: 0.1159 - val_accuracy: 1.0000\n",
      "Epoch 117/1000\n",
      "63/63 [==============================] - 0s 197us/step - loss: 0.1469 - accuracy: 0.9524 - val_loss: 0.1018 - val_accuracy: 1.0000\n",
      "Epoch 118/1000\n",
      "63/63 [==============================] - 0s 194us/step - loss: 0.1068 - accuracy: 0.9683 - val_loss: 0.1141 - val_accuracy: 1.0000\n",
      "Epoch 119/1000\n",
      "63/63 [==============================] - 0s 216us/step - loss: 0.0643 - accuracy: 0.9683 - val_loss: 0.1360 - val_accuracy: 1.0000\n",
      "Epoch 120/1000\n",
      "63/63 [==============================] - 0s 298us/step - loss: 0.0473 - accuracy: 0.9841 - val_loss: 0.1257 - val_accuracy: 1.0000\n",
      "Epoch 121/1000\n",
      "63/63 [==============================] - 0s 288us/step - loss: 0.0755 - accuracy: 0.9683 - val_loss: 0.1338 - val_accuracy: 1.0000\n",
      "Epoch 122/1000\n",
      "63/63 [==============================] - 0s 167us/step - loss: 0.0416 - accuracy: 1.0000 - val_loss: 0.1846 - val_accuracy: 0.8333\n",
      "Epoch 123/1000\n",
      "63/63 [==============================] - 0s 277us/step - loss: 0.0626 - accuracy: 0.9841 - val_loss: 0.1824 - val_accuracy: 0.9167\n",
      "Epoch 124/1000\n",
      "63/63 [==============================] - 0s 194us/step - loss: 0.0916 - accuracy: 0.9524 - val_loss: 0.1444 - val_accuracy: 0.9167\n",
      "Epoch 125/1000\n",
      "63/63 [==============================] - 0s 364us/step - loss: 0.0426 - accuracy: 0.9841 - val_loss: 0.2501 - val_accuracy: 0.8333\n",
      "Epoch 126/1000\n",
      "63/63 [==============================] - 0s 223us/step - loss: 0.0344 - accuracy: 0.9841 - val_loss: 0.1858 - val_accuracy: 0.8333\n",
      "Epoch 127/1000\n",
      "63/63 [==============================] - 0s 288us/step - loss: 0.0635 - accuracy: 0.9683 - val_loss: 0.1349 - val_accuracy: 1.0000\n",
      "Epoch 128/1000\n",
      "63/63 [==============================] - 0s 215us/step - loss: 0.1239 - accuracy: 0.9683 - val_loss: 0.0788 - val_accuracy: 1.0000\n",
      "Epoch 129/1000\n",
      "63/63 [==============================] - 0s 227us/step - loss: 0.0514 - accuracy: 0.9841 - val_loss: 0.1167 - val_accuracy: 1.0000\n",
      "Epoch 130/1000\n",
      "63/63 [==============================] - 0s 203us/step - loss: 0.0647 - accuracy: 0.9683 - val_loss: 0.0937 - val_accuracy: 1.0000\n",
      "Epoch 131/1000\n",
      "63/63 [==============================] - 0s 254us/step - loss: 0.0536 - accuracy: 0.9841 - val_loss: 0.0871 - val_accuracy: 1.0000\n",
      "Epoch 132/1000\n",
      "63/63 [==============================] - 0s 167us/step - loss: 0.0595 - accuracy: 0.9683 - val_loss: 0.0932 - val_accuracy: 1.0000\n",
      "Epoch 133/1000\n",
      "63/63 [==============================] - 0s 236us/step - loss: 0.0621 - accuracy: 0.9683 - val_loss: 0.1099 - val_accuracy: 1.0000\n",
      "Epoch 134/1000\n",
      "63/63 [==============================] - 0s 148us/step - loss: 0.0268 - accuracy: 1.0000 - val_loss: 0.1319 - val_accuracy: 1.0000\n",
      "Epoch 135/1000\n",
      "63/63 [==============================] - 0s 209us/step - loss: 0.0473 - accuracy: 0.9841 - val_loss: 0.1308 - val_accuracy: 1.0000\n",
      "Epoch 136/1000\n",
      "63/63 [==============================] - 0s 231us/step - loss: 0.0317 - accuracy: 0.9841 - val_loss: 0.1970 - val_accuracy: 0.8333\n",
      "Epoch 137/1000\n",
      "63/63 [==============================] - 0s 248us/step - loss: 0.0784 - accuracy: 0.9683 - val_loss: 0.0937 - val_accuracy: 1.0000\n",
      "Epoch 138/1000\n",
      "63/63 [==============================] - 0s 206us/step - loss: 0.0481 - accuracy: 0.9841 - val_loss: 0.1212 - val_accuracy: 1.0000\n",
      "Epoch 139/1000\n",
      "63/63 [==============================] - 0s 263us/step - loss: 0.0487 - accuracy: 0.9841 - val_loss: 0.1697 - val_accuracy: 0.9167\n",
      "Epoch 140/1000\n",
      "63/63 [==============================] - 0s 261us/step - loss: 0.1317 - accuracy: 0.9365 - val_loss: 0.1028 - val_accuracy: 1.0000\n",
      "Epoch 141/1000\n",
      "63/63 [==============================] - 0s 275us/step - loss: 0.0753 - accuracy: 0.9683 - val_loss: 0.1331 - val_accuracy: 1.0000\n",
      "Epoch 142/1000\n",
      "63/63 [==============================] - 0s 184us/step - loss: 0.0328 - accuracy: 1.0000 - val_loss: 0.1317 - val_accuracy: 1.0000\n",
      "Epoch 143/1000\n",
      "63/63 [==============================] - 0s 296us/step - loss: 0.1294 - accuracy: 0.9365 - val_loss: 0.1129 - val_accuracy: 1.0000\n",
      "Epoch 144/1000\n",
      "63/63 [==============================] - 0s 200us/step - loss: 0.0683 - accuracy: 0.9683 - val_loss: 0.0976 - val_accuracy: 1.0000\n",
      "Epoch 145/1000\n",
      "63/63 [==============================] - 0s 298us/step - loss: 0.0676 - accuracy: 0.9683 - val_loss: 0.1053 - val_accuracy: 1.0000\n",
      "Epoch 146/1000\n",
      "63/63 [==============================] - 0s 287us/step - loss: 0.0571 - accuracy: 0.9841 - val_loss: 0.1607 - val_accuracy: 0.9167\n",
      "Epoch 147/1000\n",
      "63/63 [==============================] - 0s 226us/step - loss: 0.0883 - accuracy: 0.9841 - val_loss: 0.1608 - val_accuracy: 0.9167\n",
      "Epoch 148/1000\n",
      "63/63 [==============================] - 0s 282us/step - loss: 0.0774 - accuracy: 0.9841 - val_loss: 0.1267 - val_accuracy: 1.0000\n",
      "Epoch 149/1000\n",
      "63/63 [==============================] - 0s 227us/step - loss: 0.0697 - accuracy: 0.9683 - val_loss: 0.1720 - val_accuracy: 0.9167\n",
      "Epoch 150/1000\n",
      "63/63 [==============================] - 0s 319us/step - loss: 0.0347 - accuracy: 1.0000 - val_loss: 0.2226 - val_accuracy: 0.8333\n",
      "Epoch 151/1000\n",
      "63/63 [==============================] - 0s 192us/step - loss: 0.0533 - accuracy: 0.9841 - val_loss: 0.1491 - val_accuracy: 0.9167\n",
      "Epoch 152/1000\n",
      "63/63 [==============================] - 0s 263us/step - loss: 0.0907 - accuracy: 0.9683 - val_loss: 0.1419 - val_accuracy: 0.9167\n",
      "Epoch 153/1000\n",
      "63/63 [==============================] - 0s 223us/step - loss: 0.0498 - accuracy: 0.9841 - val_loss: 0.1370 - val_accuracy: 1.0000\n",
      "Epoch 154/1000\n",
      "63/63 [==============================] - 0s 281us/step - loss: 0.0494 - accuracy: 0.9841 - val_loss: 0.1779 - val_accuracy: 0.8333\n",
      "Epoch 155/1000\n",
      "63/63 [==============================] - 0s 223us/step - loss: 0.0757 - accuracy: 0.9524 - val_loss: 0.1724 - val_accuracy: 0.9167\n",
      "Epoch 156/1000\n",
      "63/63 [==============================] - 0s 336us/step - loss: 0.0475 - accuracy: 0.9841 - val_loss: 0.2164 - val_accuracy: 0.8333\n",
      "Epoch 157/1000\n",
      "63/63 [==============================] - 0s 231us/step - loss: 0.0813 - accuracy: 0.9683 - val_loss: 0.1449 - val_accuracy: 0.9167\n",
      "Epoch 158/1000\n",
      "63/63 [==============================] - 0s 263us/step - loss: 0.0215 - accuracy: 1.0000 - val_loss: 0.1399 - val_accuracy: 0.9167\n",
      "Epoch 159/1000\n",
      "63/63 [==============================] - 0s 207us/step - loss: 0.0841 - accuracy: 0.9683 - val_loss: 0.1508 - val_accuracy: 0.9167\n",
      "Epoch 160/1000\n",
      "63/63 [==============================] - 0s 184us/step - loss: 0.0551 - accuracy: 0.9683 - val_loss: 0.1425 - val_accuracy: 0.9167\n",
      "Epoch 161/1000\n",
      "63/63 [==============================] - 0s 173us/step - loss: 0.1325 - accuracy: 0.9524 - val_loss: 0.0721 - val_accuracy: 1.0000\n",
      "Epoch 162/1000\n",
      "63/63 [==============================] - 0s 248us/step - loss: 0.0956 - accuracy: 0.9365 - val_loss: 0.0828 - val_accuracy: 1.0000\n",
      "Epoch 163/1000\n",
      "63/63 [==============================] - 0s 151us/step - loss: 0.0628 - accuracy: 0.9683 - val_loss: 0.1633 - val_accuracy: 0.9167\n",
      "Epoch 164/1000\n",
      "63/63 [==============================] - 0s 184us/step - loss: 0.0297 - accuracy: 1.0000 - val_loss: 0.1589 - val_accuracy: 0.9167\n",
      "Epoch 165/1000\n",
      "63/63 [==============================] - 0s 270us/step - loss: 0.0239 - accuracy: 1.0000 - val_loss: 0.1846 - val_accuracy: 0.8333\n",
      "Epoch 166/1000\n",
      "63/63 [==============================] - 0s 220us/step - loss: 0.0512 - accuracy: 0.9683 - val_loss: 0.1731 - val_accuracy: 0.9167\n",
      "Epoch 167/1000\n",
      "63/63 [==============================] - 0s 274us/step - loss: 0.0891 - accuracy: 0.9841 - val_loss: 0.1772 - val_accuracy: 0.8333\n",
      "Epoch 168/1000\n",
      "63/63 [==============================] - 0s 247us/step - loss: 0.0336 - accuracy: 1.0000 - val_loss: 0.1862 - val_accuracy: 0.8333\n",
      "Epoch 169/1000\n",
      "63/63 [==============================] - 0s 255us/step - loss: 0.0286 - accuracy: 1.0000 - val_loss: 0.1680 - val_accuracy: 0.9167\n",
      "Epoch 170/1000\n",
      "63/63 [==============================] - 0s 306us/step - loss: 0.0528 - accuracy: 0.9683 - val_loss: 0.1023 - val_accuracy: 1.0000\n",
      "Epoch 171/1000\n",
      "63/63 [==============================] - 0s 219us/step - loss: 0.0634 - accuracy: 0.9683 - val_loss: 0.0652 - val_accuracy: 1.0000\n",
      "Epoch 172/1000\n",
      "63/63 [==============================] - 0s 256us/step - loss: 0.0540 - accuracy: 0.9841 - val_loss: 0.1197 - val_accuracy: 1.0000\n",
      "Epoch 173/1000\n",
      "63/63 [==============================] - 0s 176us/step - loss: 0.0656 - accuracy: 0.9841 - val_loss: 0.1005 - val_accuracy: 1.0000\n",
      "Epoch 174/1000\n",
      "63/63 [==============================] - 0s 305us/step - loss: 0.0315 - accuracy: 1.0000 - val_loss: 0.1408 - val_accuracy: 0.9167\n",
      "Epoch 175/1000\n",
      "63/63 [==============================] - 0s 207us/step - loss: 0.0911 - accuracy: 0.9683 - val_loss: 0.0858 - val_accuracy: 1.0000\n",
      "Epoch 176/1000\n",
      "63/63 [==============================] - 0s 289us/step - loss: 0.0495 - accuracy: 0.9841 - val_loss: 0.1477 - val_accuracy: 0.9167\n",
      "Epoch 177/1000\n",
      "63/63 [==============================] - 0s 199us/step - loss: 0.0387 - accuracy: 0.9841 - val_loss: 0.2094 - val_accuracy: 0.8333\n",
      "Epoch 178/1000\n",
      "63/63 [==============================] - 0s 232us/step - loss: 0.0550 - accuracy: 0.9841 - val_loss: 0.1086 - val_accuracy: 1.0000\n",
      "Epoch 179/1000\n",
      "63/63 [==============================] - 0s 155us/step - loss: 0.0355 - accuracy: 1.0000 - val_loss: 0.1120 - val_accuracy: 1.0000\n",
      "Epoch 180/1000\n",
      "63/63 [==============================] - 0s 298us/step - loss: 0.0966 - accuracy: 0.9683 - val_loss: 0.0704 - val_accuracy: 1.0000\n",
      "Epoch 181/1000\n",
      "63/63 [==============================] - 0s 183us/step - loss: 0.0272 - accuracy: 1.0000 - val_loss: 0.1073 - val_accuracy: 1.0000\n",
      "Epoch 182/1000\n",
      "63/63 [==============================] - 0s 236us/step - loss: 0.0447 - accuracy: 0.9841 - val_loss: 0.1596 - val_accuracy: 0.9167\n",
      "Epoch 183/1000\n",
      "63/63 [==============================] - 0s 177us/step - loss: 0.1063 - accuracy: 0.9683 - val_loss: 0.1578 - val_accuracy: 0.9167\n",
      "Epoch 184/1000\n",
      "63/63 [==============================] - 0s 247us/step - loss: 0.0309 - accuracy: 1.0000 - val_loss: 0.1655 - val_accuracy: 0.9167\n",
      "Epoch 185/1000\n",
      "63/63 [==============================] - 0s 190us/step - loss: 0.0266 - accuracy: 1.0000 - val_loss: 0.2095 - val_accuracy: 0.8333\n",
      "Epoch 186/1000\n",
      "63/63 [==============================] - 0s 193us/step - loss: 0.0304 - accuracy: 1.0000 - val_loss: 0.1806 - val_accuracy: 0.8333\n",
      "Epoch 187/1000\n",
      "63/63 [==============================] - 0s 259us/step - loss: 0.0568 - accuracy: 0.9841 - val_loss: 0.1397 - val_accuracy: 0.9167\n",
      "Epoch 188/1000\n",
      "63/63 [==============================] - 0s 199us/step - loss: 0.0237 - accuracy: 1.0000 - val_loss: 0.1709 - val_accuracy: 0.8333\n",
      "Epoch 189/1000\n",
      "63/63 [==============================] - 0s 263us/step - loss: 0.0372 - accuracy: 1.0000 - val_loss: 0.1612 - val_accuracy: 0.9167\n",
      "Epoch 190/1000\n",
      "63/63 [==============================] - 0s 140us/step - loss: 0.0536 - accuracy: 0.9841 - val_loss: 0.2197 - val_accuracy: 0.8333\n",
      "Epoch 191/1000\n",
      "63/63 [==============================] - 0s 277us/step - loss: 0.0215 - accuracy: 1.0000 - val_loss: 0.2236 - val_accuracy: 0.8333\n",
      "Epoch 192/1000\n",
      "63/63 [==============================] - 0s 176us/step - loss: 0.0406 - accuracy: 0.9841 - val_loss: 0.1898 - val_accuracy: 0.8333\n",
      "Epoch 193/1000\n",
      "63/63 [==============================] - 0s 272us/step - loss: 0.1037 - accuracy: 0.9365 - val_loss: 0.0990 - val_accuracy: 1.0000\n",
      "Epoch 194/1000\n",
      "63/63 [==============================] - 0s 247us/step - loss: 0.0424 - accuracy: 0.9841 - val_loss: 0.0693 - val_accuracy: 1.0000\n",
      "Epoch 195/1000\n",
      "63/63 [==============================] - 0s 352us/step - loss: 0.0406 - accuracy: 0.9841 - val_loss: 0.1305 - val_accuracy: 1.0000\n",
      "Epoch 196/1000\n",
      "63/63 [==============================] - 0s 191us/step - loss: 0.1024 - accuracy: 0.9683 - val_loss: 0.1278 - val_accuracy: 1.0000\n",
      "Epoch 197/1000\n",
      "63/63 [==============================] - 0s 246us/step - loss: 0.0976 - accuracy: 0.9524 - val_loss: 0.1962 - val_accuracy: 0.8333\n",
      "Epoch 198/1000\n",
      "63/63 [==============================] - 0s 177us/step - loss: 0.0879 - accuracy: 0.9524 - val_loss: 0.2067 - val_accuracy: 0.8333\n",
      "Epoch 199/1000\n",
      "63/63 [==============================] - 0s 352us/step - loss: 0.0493 - accuracy: 0.9841 - val_loss: 0.1250 - val_accuracy: 1.0000\n",
      "Epoch 200/1000\n",
      "63/63 [==============================] - 0s 199us/step - loss: 0.0566 - accuracy: 0.9841 - val_loss: 0.1291 - val_accuracy: 1.0000\n",
      "Epoch 201/1000\n",
      "63/63 [==============================] - 0s 250us/step - loss: 0.0342 - accuracy: 1.0000 - val_loss: 0.1366 - val_accuracy: 0.9167\n",
      "Epoch 202/1000\n",
      "63/63 [==============================] - 0s 167us/step - loss: 0.0402 - accuracy: 0.9683 - val_loss: 0.2176 - val_accuracy: 0.8333\n",
      "Epoch 203/1000\n",
      "63/63 [==============================] - 0s 252us/step - loss: 0.0267 - accuracy: 1.0000 - val_loss: 0.1642 - val_accuracy: 0.9167\n",
      "Epoch 204/1000\n",
      "63/63 [==============================] - 0s 190us/step - loss: 0.0322 - accuracy: 0.9841 - val_loss: 0.1431 - val_accuracy: 0.9167\n",
      "Epoch 205/1000\n",
      "63/63 [==============================] - 0s 285us/step - loss: 0.1149 - accuracy: 0.9365 - val_loss: 0.1427 - val_accuracy: 0.9167\n",
      "Epoch 206/1000\n",
      "63/63 [==============================] - 0s 161us/step - loss: 0.0308 - accuracy: 1.0000 - val_loss: 0.2055 - val_accuracy: 0.8333\n",
      "Epoch 207/1000\n",
      "63/63 [==============================] - 0s 216us/step - loss: 0.0176 - accuracy: 1.0000 - val_loss: 0.1821 - val_accuracy: 0.8333\n",
      "Epoch 208/1000\n",
      "63/63 [==============================] - 0s 200us/step - loss: 0.0339 - accuracy: 1.0000 - val_loss: 0.1473 - val_accuracy: 0.9167\n",
      "Epoch 209/1000\n",
      "63/63 [==============================] - 0s 183us/step - loss: 0.0799 - accuracy: 0.9683 - val_loss: 0.1719 - val_accuracy: 0.8333\n",
      "Epoch 210/1000\n",
      "63/63 [==============================] - 0s 264us/step - loss: 0.0464 - accuracy: 0.9841 - val_loss: 0.1796 - val_accuracy: 0.8333\n",
      "Epoch 211/1000\n",
      "63/63 [==============================] - 0s 206us/step - loss: 0.0702 - accuracy: 0.9683 - val_loss: 0.1893 - val_accuracy: 0.8333\n",
      "Epoch 212/1000\n",
      "63/63 [==============================] - 0s 277us/step - loss: 0.0431 - accuracy: 0.9841 - val_loss: 0.1806 - val_accuracy: 0.8333\n",
      "Epoch 213/1000\n",
      "63/63 [==============================] - 0s 240us/step - loss: 0.0686 - accuracy: 0.9841 - val_loss: 0.1526 - val_accuracy: 0.9167\n",
      "Epoch 214/1000\n",
      "63/63 [==============================] - 0s 215us/step - loss: 0.0378 - accuracy: 0.9841 - val_loss: 0.1205 - val_accuracy: 1.0000\n",
      "Epoch 215/1000\n",
      "63/63 [==============================] - 0s 246us/step - loss: 0.0414 - accuracy: 0.9841 - val_loss: 0.1398 - val_accuracy: 0.9167\n",
      "Epoch 216/1000\n",
      "63/63 [==============================] - 0s 239us/step - loss: 0.0456 - accuracy: 0.9841 - val_loss: 0.1512 - val_accuracy: 0.9167\n",
      "Epoch 217/1000\n",
      "63/63 [==============================] - 0s 248us/step - loss: 0.0335 - accuracy: 1.0000 - val_loss: 0.1583 - val_accuracy: 0.9167\n",
      "Epoch 218/1000\n",
      "63/63 [==============================] - 0s 239us/step - loss: 0.0925 - accuracy: 0.9683 - val_loss: 0.1236 - val_accuracy: 1.0000\n",
      "Epoch 219/1000\n",
      "63/63 [==============================] - 0s 296us/step - loss: 0.0395 - accuracy: 1.0000 - val_loss: 0.1071 - val_accuracy: 1.0000\n",
      "Epoch 220/1000\n",
      "63/63 [==============================] - 0s 224us/step - loss: 0.0265 - accuracy: 1.0000 - val_loss: 0.1241 - val_accuracy: 1.0000\n",
      "Epoch 221/1000\n",
      "63/63 [==============================] - 0s 246us/step - loss: 0.0458 - accuracy: 0.9841 - val_loss: 0.1013 - val_accuracy: 1.0000\n",
      "Epoch 222/1000\n",
      "63/63 [==============================] - 0s 272us/step - loss: 0.1369 - accuracy: 0.9524 - val_loss: 0.1041 - val_accuracy: 1.0000\n",
      "Epoch 223/1000\n",
      "63/63 [==============================] - 0s 332us/step - loss: 0.0846 - accuracy: 0.9683 - val_loss: 0.1111 - val_accuracy: 1.0000\n",
      "Epoch 224/1000\n",
      "63/63 [==============================] - 0s 237us/step - loss: 0.0196 - accuracy: 1.0000 - val_loss: 0.1302 - val_accuracy: 0.9167\n",
      "Epoch 225/1000\n",
      "63/63 [==============================] - 0s 259us/step - loss: 0.0718 - accuracy: 0.9683 - val_loss: 0.1370 - val_accuracy: 0.9167\n",
      "Epoch 226/1000\n",
      "63/63 [==============================] - 0s 215us/step - loss: 0.0492 - accuracy: 0.9841 - val_loss: 0.1762 - val_accuracy: 0.8333\n",
      "Epoch 227/1000\n",
      "63/63 [==============================] - 0s 298us/step - loss: 0.0267 - accuracy: 1.0000 - val_loss: 0.1586 - val_accuracy: 0.9167\n",
      "Epoch 228/1000\n",
      "63/63 [==============================] - 0s 311us/step - loss: 0.0325 - accuracy: 1.0000 - val_loss: 0.1465 - val_accuracy: 0.9167\n",
      "Epoch 229/1000\n",
      "63/63 [==============================] - 0s 240us/step - loss: 0.0449 - accuracy: 1.0000 - val_loss: 0.1855 - val_accuracy: 0.8333\n",
      "Epoch 230/1000\n",
      "63/63 [==============================] - 0s 288us/step - loss: 0.0368 - accuracy: 0.9841 - val_loss: 0.1760 - val_accuracy: 0.8333\n",
      "Epoch 231/1000\n",
      "63/63 [==============================] - 0s 239us/step - loss: 0.0342 - accuracy: 0.9841 - val_loss: 0.1447 - val_accuracy: 0.9167\n",
      "Epoch 232/1000\n",
      "63/63 [==============================] - 0s 309us/step - loss: 0.1884 - accuracy: 0.9524 - val_loss: 0.1178 - val_accuracy: 1.0000\n",
      "Epoch 233/1000\n",
      "63/63 [==============================] - 0s 209us/step - loss: 0.0469 - accuracy: 0.9841 - val_loss: 0.1900 - val_accuracy: 0.8333\n",
      "Epoch 234/1000\n",
      "63/63 [==============================] - 0s 287us/step - loss: 0.0390 - accuracy: 0.9841 - val_loss: 0.1826 - val_accuracy: 0.8333\n",
      "Epoch 235/1000\n",
      "63/63 [==============================] - 0s 232us/step - loss: 0.0757 - accuracy: 0.9841 - val_loss: 0.1933 - val_accuracy: 0.8333\n",
      "Epoch 236/1000\n",
      "63/63 [==============================] - 0s 232us/step - loss: 0.1200 - accuracy: 0.9683 - val_loss: 0.1264 - val_accuracy: 1.0000\n",
      "Epoch 237/1000\n",
      "63/63 [==============================] - 0s 168us/step - loss: 0.0408 - accuracy: 0.9841 - val_loss: 0.1823 - val_accuracy: 0.8333\n",
      "Epoch 238/1000\n",
      "63/63 [==============================] - 0s 274us/step - loss: 0.0439 - accuracy: 0.9841 - val_loss: 0.2402 - val_accuracy: 0.8333\n",
      "Epoch 239/1000\n",
      "63/63 [==============================] - 0s 207us/step - loss: 0.0473 - accuracy: 0.9841 - val_loss: 0.2024 - val_accuracy: 0.8333\n",
      "Epoch 240/1000\n",
      "63/63 [==============================] - 0s 227us/step - loss: 0.0168 - accuracy: 1.0000 - val_loss: 0.1867 - val_accuracy: 0.8333\n",
      "Epoch 241/1000\n",
      "63/63 [==============================] - 0s 167us/step - loss: 0.0366 - accuracy: 1.0000 - val_loss: 0.1731 - val_accuracy: 0.8333\n",
      "Epoch 242/1000\n",
      "63/63 [==============================] - 0s 267us/step - loss: 0.0443 - accuracy: 0.9841 - val_loss: 0.2541 - val_accuracy: 0.8333\n",
      "Epoch 243/1000\n",
      "63/63 [==============================] - 0s 184us/step - loss: 0.0237 - accuracy: 1.0000 - val_loss: 0.2177 - val_accuracy: 0.8333\n",
      "Epoch 244/1000\n",
      "63/63 [==============================] - 0s 225us/step - loss: 0.0227 - accuracy: 1.0000 - val_loss: 0.1751 - val_accuracy: 0.8333\n",
      "Epoch 245/1000\n",
      "63/63 [==============================] - 0s 165us/step - loss: 0.0272 - accuracy: 1.0000 - val_loss: 0.1472 - val_accuracy: 0.9167\n",
      "Epoch 246/1000\n",
      "63/63 [==============================] - 0s 248us/step - loss: 0.0301 - accuracy: 1.0000 - val_loss: 0.1333 - val_accuracy: 0.9167\n",
      "Epoch 247/1000\n",
      "63/63 [==============================] - 0s 231us/step - loss: 0.0561 - accuracy: 0.9841 - val_loss: 0.0931 - val_accuracy: 1.0000\n",
      "Epoch 248/1000\n",
      "63/63 [==============================] - 0s 240us/step - loss: 0.0490 - accuracy: 0.9841 - val_loss: 0.1088 - val_accuracy: 1.0000\n",
      "Epoch 249/1000\n",
      "63/63 [==============================] - 0s 240us/step - loss: 0.0824 - accuracy: 0.9524 - val_loss: 0.1745 - val_accuracy: 0.8333\n",
      "Epoch 250/1000\n",
      "63/63 [==============================] - 0s 184us/step - loss: 0.0438 - accuracy: 0.9683 - val_loss: 0.1952 - val_accuracy: 0.8333\n",
      "Epoch 251/1000\n",
      "63/63 [==============================] - 0s 243us/step - loss: 0.0311 - accuracy: 1.0000 - val_loss: 0.1773 - val_accuracy: 0.8333\n",
      "Epoch 252/1000\n",
      "63/63 [==============================] - 0s 288us/step - loss: 0.0209 - accuracy: 1.0000 - val_loss: 0.2095 - val_accuracy: 0.8333\n",
      "Epoch 253/1000\n",
      "63/63 [==============================] - 0s 255us/step - loss: 0.0849 - accuracy: 0.9683 - val_loss: 0.1273 - val_accuracy: 0.9167\n",
      "Epoch 254/1000\n",
      "63/63 [==============================] - 0s 311us/step - loss: 0.0199 - accuracy: 1.0000 - val_loss: 0.1405 - val_accuracy: 0.9167\n",
      "Epoch 255/1000\n",
      "63/63 [==============================] - 0s 247us/step - loss: 0.0345 - accuracy: 1.0000 - val_loss: 0.1215 - val_accuracy: 1.0000\n",
      "Epoch 256/1000\n",
      "63/63 [==============================] - 0s 312us/step - loss: 0.0393 - accuracy: 0.9841 - val_loss: 0.1116 - val_accuracy: 1.0000\n",
      "Epoch 257/1000\n",
      "63/63 [==============================] - 0s 321us/step - loss: 0.0320 - accuracy: 1.0000 - val_loss: 0.1003 - val_accuracy: 1.0000\n",
      "Epoch 258/1000\n",
      "63/63 [==============================] - 0s 332us/step - loss: 0.0779 - accuracy: 0.9683 - val_loss: 0.1065 - val_accuracy: 1.0000\n",
      "Epoch 259/1000\n",
      "63/63 [==============================] - 0s 343us/step - loss: 0.0363 - accuracy: 0.9841 - val_loss: 0.0873 - val_accuracy: 1.0000\n",
      "Epoch 260/1000\n",
      "63/63 [==============================] - 0s 273us/step - loss: 0.0453 - accuracy: 0.9841 - val_loss: 0.0684 - val_accuracy: 1.0000\n",
      "Epoch 261/1000\n",
      "63/63 [==============================] - 0s 172us/step - loss: 0.0269 - accuracy: 1.0000 - val_loss: 0.0821 - val_accuracy: 1.0000\n",
      "Epoch 262/1000\n",
      "63/63 [==============================] - 0s 246us/step - loss: 0.0364 - accuracy: 0.9841 - val_loss: 0.1606 - val_accuracy: 0.9167\n",
      "Epoch 263/1000\n",
      "63/63 [==============================] - 0s 230us/step - loss: 0.0311 - accuracy: 0.9841 - val_loss: 0.2092 - val_accuracy: 0.8333\n",
      "Epoch 264/1000\n",
      "63/63 [==============================] - 0s 300us/step - loss: 0.0927 - accuracy: 0.9683 - val_loss: 0.1098 - val_accuracy: 1.0000\n",
      "Epoch 265/1000\n",
      "63/63 [==============================] - 0s 278us/step - loss: 0.0610 - accuracy: 0.9524 - val_loss: 0.1823 - val_accuracy: 0.8333\n",
      "Epoch 266/1000\n",
      "63/63 [==============================] - 0s 283us/step - loss: 0.0236 - accuracy: 1.0000 - val_loss: 0.1316 - val_accuracy: 0.9167\n",
      "Epoch 267/1000\n",
      "63/63 [==============================] - 0s 194us/step - loss: 0.0119 - accuracy: 1.0000 - val_loss: 0.1219 - val_accuracy: 1.0000\n",
      "Epoch 268/1000\n",
      "63/63 [==============================] - 0s 280us/step - loss: 0.0148 - accuracy: 1.0000 - val_loss: 0.1158 - val_accuracy: 1.0000\n",
      "Epoch 269/1000\n",
      "63/63 [==============================] - 0s 199us/step - loss: 0.1033 - accuracy: 0.9683 - val_loss: 0.1681 - val_accuracy: 0.8333\n",
      "Epoch 270/1000\n",
      "63/63 [==============================] - 0s 256us/step - loss: 0.0380 - accuracy: 0.9841 - val_loss: 0.2250 - val_accuracy: 0.8333\n",
      "Epoch 271/1000\n",
      "63/63 [==============================] - 0s 292us/step - loss: 0.0206 - accuracy: 1.0000 - val_loss: 0.2240 - val_accuracy: 0.8333\n",
      "Epoch 272/1000\n",
      "63/63 [==============================] - 0s 241us/step - loss: 0.0418 - accuracy: 0.9841 - val_loss: 0.1983 - val_accuracy: 0.8333\n",
      "Epoch 273/1000\n",
      "63/63 [==============================] - 0s 247us/step - loss: 0.0185 - accuracy: 1.0000 - val_loss: 0.1919 - val_accuracy: 0.8333\n",
      "Epoch 274/1000\n",
      "63/63 [==============================] - 0s 212us/step - loss: 0.0470 - accuracy: 0.9841 - val_loss: 0.1604 - val_accuracy: 0.9167\n",
      "Epoch 275/1000\n",
      "63/63 [==============================] - 0s 236us/step - loss: 0.0367 - accuracy: 0.9841 - val_loss: 0.1816 - val_accuracy: 0.8333\n",
      "Epoch 276/1000\n",
      "63/63 [==============================] - 0s 224us/step - loss: 0.0246 - accuracy: 1.0000 - val_loss: 0.1779 - val_accuracy: 0.8333\n",
      "Epoch 277/1000\n",
      "63/63 [==============================] - 0s 257us/step - loss: 0.0247 - accuracy: 0.9841 - val_loss: 0.1583 - val_accuracy: 0.9167\n",
      "Epoch 278/1000\n",
      "63/63 [==============================] - 0s 300us/step - loss: 0.0193 - accuracy: 1.0000 - val_loss: 0.2015 - val_accuracy: 0.8333\n",
      "Epoch 279/1000\n",
      "63/63 [==============================] - 0s 287us/step - loss: 0.0283 - accuracy: 0.9841 - val_loss: 0.1533 - val_accuracy: 0.9167\n",
      "Epoch 280/1000\n",
      "63/63 [==============================] - 0s 239us/step - loss: 0.0409 - accuracy: 0.9841 - val_loss: 0.2221 - val_accuracy: 0.8333\n",
      "Epoch 281/1000\n",
      "63/63 [==============================] - 0s 303us/step - loss: 0.0364 - accuracy: 0.9841 - val_loss: 0.1646 - val_accuracy: 0.8333\n",
      "Epoch 282/1000\n",
      "63/63 [==============================] - 0s 199us/step - loss: 0.0570 - accuracy: 0.9524 - val_loss: 0.1169 - val_accuracy: 1.0000\n",
      "Epoch 283/1000\n",
      "63/63 [==============================] - 0s 332us/step - loss: 0.0662 - accuracy: 0.9841 - val_loss: 0.1475 - val_accuracy: 0.9167\n",
      "Epoch 284/1000\n",
      "63/63 [==============================] - 0s 303us/step - loss: 0.0250 - accuracy: 1.0000 - val_loss: 0.1033 - val_accuracy: 1.0000\n",
      "Epoch 285/1000\n",
      "63/63 [==============================] - 0s 315us/step - loss: 0.0275 - accuracy: 1.0000 - val_loss: 0.1546 - val_accuracy: 0.9167\n",
      "Epoch 286/1000\n",
      "63/63 [==============================] - 0s 329us/step - loss: 0.0374 - accuracy: 0.9841 - val_loss: 0.1616 - val_accuracy: 0.9167\n",
      "Epoch 287/1000\n",
      "63/63 [==============================] - 0s 223us/step - loss: 0.0252 - accuracy: 0.9841 - val_loss: 0.2033 - val_accuracy: 0.8333\n",
      "Epoch 288/1000\n",
      "63/63 [==============================] - 0s 290us/step - loss: 0.0537 - accuracy: 0.9683 - val_loss: 0.2018 - val_accuracy: 0.8333\n",
      "Epoch 289/1000\n",
      "63/63 [==============================] - 0s 263us/step - loss: 0.0413 - accuracy: 0.9683 - val_loss: 0.1462 - val_accuracy: 0.9167\n",
      "Epoch 290/1000\n",
      "63/63 [==============================] - 0s 215us/step - loss: 0.0293 - accuracy: 0.9841 - val_loss: 0.1213 - val_accuracy: 0.9167\n",
      "Epoch 291/1000\n",
      "63/63 [==============================] - 0s 304us/step - loss: 0.0212 - accuracy: 1.0000 - val_loss: 0.1236 - val_accuracy: 0.9167\n",
      "Epoch 292/1000\n",
      "63/63 [==============================] - 0s 271us/step - loss: 0.0254 - accuracy: 0.9841 - val_loss: 0.1720 - val_accuracy: 0.8333\n",
      "Epoch 293/1000\n",
      "63/63 [==============================] - 0s 255us/step - loss: 0.0243 - accuracy: 1.0000 - val_loss: 0.1688 - val_accuracy: 0.8333\n",
      "Epoch 294/1000\n",
      "63/63 [==============================] - 0s 216us/step - loss: 0.0683 - accuracy: 0.9683 - val_loss: 0.1085 - val_accuracy: 1.0000\n",
      "Epoch 295/1000\n",
      "63/63 [==============================] - 0s 339us/step - loss: 0.0128 - accuracy: 1.0000 - val_loss: 0.1061 - val_accuracy: 1.0000\n",
      "Epoch 296/1000\n",
      "63/63 [==============================] - 0s 226us/step - loss: 0.0402 - accuracy: 0.9683 - val_loss: 0.1154 - val_accuracy: 1.0000\n",
      "Epoch 297/1000\n",
      "63/63 [==============================] - 0s 302us/step - loss: 0.0339 - accuracy: 1.0000 - val_loss: 0.1194 - val_accuracy: 0.9167\n",
      "Epoch 298/1000\n",
      "63/63 [==============================] - 0s 330us/step - loss: 0.0300 - accuracy: 0.9841 - val_loss: 0.1278 - val_accuracy: 0.9167\n",
      "Epoch 299/1000\n",
      "63/63 [==============================] - 0s 206us/step - loss: 0.0278 - accuracy: 0.9841 - val_loss: 0.0957 - val_accuracy: 1.0000\n",
      "Epoch 300/1000\n",
      "63/63 [==============================] - 0s 301us/step - loss: 0.0343 - accuracy: 0.9841 - val_loss: 0.1166 - val_accuracy: 1.0000\n",
      "Epoch 301/1000\n",
      "63/63 [==============================] - 0s 215us/step - loss: 0.0335 - accuracy: 0.9841 - val_loss: 0.1349 - val_accuracy: 0.9167\n",
      "Epoch 302/1000\n",
      "63/63 [==============================] - 0s 289us/step - loss: 0.0115 - accuracy: 1.0000 - val_loss: 0.1188 - val_accuracy: 0.9167\n",
      "Epoch 303/1000\n",
      "63/63 [==============================] - 0s 183us/step - loss: 0.0154 - accuracy: 1.0000 - val_loss: 0.1424 - val_accuracy: 0.9167\n",
      "Epoch 304/1000\n",
      "63/63 [==============================] - 0s 309us/step - loss: 0.0105 - accuracy: 1.0000 - val_loss: 0.1531 - val_accuracy: 0.9167\n",
      "Epoch 305/1000\n",
      "63/63 [==============================] - 0s 220us/step - loss: 0.0235 - accuracy: 1.0000 - val_loss: 0.1508 - val_accuracy: 0.9167\n",
      "Epoch 306/1000\n",
      "63/63 [==============================] - 0s 275us/step - loss: 0.0477 - accuracy: 0.9683 - val_loss: 0.1448 - val_accuracy: 0.9167\n",
      "Epoch 307/1000\n",
      "63/63 [==============================] - 0s 240us/step - loss: 0.0111 - accuracy: 1.0000 - val_loss: 0.1565 - val_accuracy: 0.9167\n",
      "Epoch 308/1000\n",
      "63/63 [==============================] - 0s 204us/step - loss: 0.0188 - accuracy: 1.0000 - val_loss: 0.1711 - val_accuracy: 0.8333\n",
      "Epoch 309/1000\n",
      "63/63 [==============================] - 0s 216us/step - loss: 0.0256 - accuracy: 1.0000 - val_loss: 0.1727 - val_accuracy: 0.8333\n",
      "Epoch 310/1000\n",
      "63/63 [==============================] - 0s 247us/step - loss: 0.0425 - accuracy: 0.9683 - val_loss: 0.1915 - val_accuracy: 0.8333\n",
      "Epoch 311/1000\n",
      "63/63 [==============================] - 0s 186us/step - loss: 0.0141 - accuracy: 1.0000 - val_loss: 0.2078 - val_accuracy: 0.8333\n",
      "Epoch 312/1000\n",
      "63/63 [==============================] - 0s 287us/step - loss: 0.0296 - accuracy: 0.9841 - val_loss: 0.1427 - val_accuracy: 0.9167\n",
      "Epoch 313/1000\n",
      "63/63 [==============================] - 0s 209us/step - loss: 0.0366 - accuracy: 0.9841 - val_loss: 0.2133 - val_accuracy: 0.8333\n",
      "Epoch 314/1000\n",
      "63/63 [==============================] - 0s 291us/step - loss: 0.0445 - accuracy: 0.9841 - val_loss: 0.1372 - val_accuracy: 0.9167\n",
      "Epoch 315/1000\n",
      "63/63 [==============================] - 0s 218us/step - loss: 0.0197 - accuracy: 1.0000 - val_loss: 0.1318 - val_accuracy: 0.9167\n",
      "Epoch 316/1000\n",
      "63/63 [==============================] - 0s 274us/step - loss: 0.0112 - accuracy: 1.0000 - val_loss: 0.1386 - val_accuracy: 0.9167\n",
      "Epoch 317/1000\n",
      "63/63 [==============================] - 0s 291us/step - loss: 0.0485 - accuracy: 0.9841 - val_loss: 0.0774 - val_accuracy: 1.0000\n",
      "Epoch 318/1000\n",
      "63/63 [==============================] - 0s 296us/step - loss: 0.0805 - accuracy: 0.9683 - val_loss: 0.0834 - val_accuracy: 1.0000\n",
      "Epoch 319/1000\n",
      "63/63 [==============================] - 0s 282us/step - loss: 0.0071 - accuracy: 1.0000 - val_loss: 0.0848 - val_accuracy: 1.0000\n",
      "Epoch 320/1000\n",
      "63/63 [==============================] - 0s 225us/step - loss: 0.0630 - accuracy: 0.9841 - val_loss: 0.1144 - val_accuracy: 0.9167\n",
      "Epoch 321/1000\n",
      "63/63 [==============================] - 0s 398us/step - loss: 0.0412 - accuracy: 0.9841 - val_loss: 0.1902 - val_accuracy: 0.8333\n",
      "Epoch 322/1000\n",
      "63/63 [==============================] - 0s 287us/step - loss: 0.0211 - accuracy: 0.9841 - val_loss: 0.1488 - val_accuracy: 0.9167\n",
      "Epoch 323/1000\n",
      "63/63 [==============================] - 0s 192us/step - loss: 0.0203 - accuracy: 1.0000 - val_loss: 0.1383 - val_accuracy: 0.9167\n",
      "Epoch 324/1000\n",
      "63/63 [==============================] - 0s 224us/step - loss: 0.0153 - accuracy: 1.0000 - val_loss: 0.1254 - val_accuracy: 0.9167\n",
      "Epoch 325/1000\n",
      "63/63 [==============================] - 0s 176us/step - loss: 0.0236 - accuracy: 0.9841 - val_loss: 0.1092 - val_accuracy: 1.0000\n",
      "Epoch 326/1000\n",
      "63/63 [==============================] - 0s 271us/step - loss: 0.0453 - accuracy: 1.0000 - val_loss: 0.1157 - val_accuracy: 0.9167\n",
      "Epoch 327/1000\n",
      "63/63 [==============================] - 0s 242us/step - loss: 0.0185 - accuracy: 1.0000 - val_loss: 0.1615 - val_accuracy: 0.9167\n",
      "Epoch 328/1000\n",
      "63/63 [==============================] - 0s 289us/step - loss: 0.0137 - accuracy: 1.0000 - val_loss: 0.1451 - val_accuracy: 0.9167\n",
      "Epoch 329/1000\n",
      "63/63 [==============================] - 0s 239us/step - loss: 0.0207 - accuracy: 0.9841 - val_loss: 0.1103 - val_accuracy: 1.0000\n",
      "Epoch 330/1000\n",
      "63/63 [==============================] - 0s 261us/step - loss: 0.0296 - accuracy: 0.9841 - val_loss: 0.1297 - val_accuracy: 0.9167\n",
      "Epoch 331/1000\n",
      "63/63 [==============================] - 0s 224us/step - loss: 0.0256 - accuracy: 0.9841 - val_loss: 0.1559 - val_accuracy: 0.9167\n",
      "Epoch 332/1000\n",
      "63/63 [==============================] - 0s 282us/step - loss: 0.0387 - accuracy: 0.9841 - val_loss: 0.0754 - val_accuracy: 1.0000\n",
      "Epoch 333/1000\n",
      "63/63 [==============================] - 0s 274us/step - loss: 0.0389 - accuracy: 0.9683 - val_loss: 0.0625 - val_accuracy: 1.0000\n",
      "Epoch 334/1000\n",
      "63/63 [==============================] - 0s 241us/step - loss: 0.0563 - accuracy: 0.9683 - val_loss: 0.0973 - val_accuracy: 1.0000\n",
      "Epoch 335/1000\n",
      "63/63 [==============================] - 0s 319us/step - loss: 0.0676 - accuracy: 0.9524 - val_loss: 0.0644 - val_accuracy: 1.0000\n",
      "Epoch 336/1000\n",
      "63/63 [==============================] - 0s 187us/step - loss: 0.0505 - accuracy: 0.9841 - val_loss: 0.1251 - val_accuracy: 0.9167\n",
      "Epoch 337/1000\n",
      "63/63 [==============================] - 0s 249us/step - loss: 0.0184 - accuracy: 1.0000 - val_loss: 0.1505 - val_accuracy: 0.9167\n",
      "Epoch 338/1000\n",
      "63/63 [==============================] - 0s 376us/step - loss: 0.0224 - accuracy: 0.9841 - val_loss: 0.1823 - val_accuracy: 0.8333\n",
      "Epoch 339/1000\n",
      "63/63 [==============================] - 0s 270us/step - loss: 0.0174 - accuracy: 1.0000 - val_loss: 0.1631 - val_accuracy: 0.9167\n",
      "Epoch 340/1000\n",
      "63/63 [==============================] - 0s 287us/step - loss: 0.0175 - accuracy: 1.0000 - val_loss: 0.2073 - val_accuracy: 0.8333\n",
      "Epoch 341/1000\n",
      "63/63 [==============================] - 0s 249us/step - loss: 0.0103 - accuracy: 1.0000 - val_loss: 0.1992 - val_accuracy: 0.8333\n",
      "Epoch 342/1000\n",
      "63/63 [==============================] - 0s 269us/step - loss: 0.0647 - accuracy: 0.9841 - val_loss: 0.1512 - val_accuracy: 0.9167\n",
      "Epoch 343/1000\n",
      "63/63 [==============================] - 0s 282us/step - loss: 0.0094 - accuracy: 1.0000 - val_loss: 0.1711 - val_accuracy: 0.8333\n",
      "Epoch 344/1000\n",
      "63/63 [==============================] - 0s 203us/step - loss: 0.0350 - accuracy: 0.9841 - val_loss: 0.1758 - val_accuracy: 0.8333\n",
      "Epoch 345/1000\n",
      "63/63 [==============================] - 0s 203us/step - loss: 0.0095 - accuracy: 1.0000 - val_loss: 0.1736 - val_accuracy: 0.8333\n",
      "Epoch 346/1000\n",
      "63/63 [==============================] - 0s 286us/step - loss: 0.0107 - accuracy: 1.0000 - val_loss: 0.1768 - val_accuracy: 0.8333\n",
      "Epoch 347/1000\n",
      "63/63 [==============================] - 0s 242us/step - loss: 0.0145 - accuracy: 1.0000 - val_loss: 0.1764 - val_accuracy: 0.8333\n",
      "Epoch 348/1000\n",
      "63/63 [==============================] - 0s 222us/step - loss: 0.0115 - accuracy: 1.0000 - val_loss: 0.2069 - val_accuracy: 0.8333\n",
      "Epoch 349/1000\n",
      "63/63 [==============================] - 0s 209us/step - loss: 0.0126 - accuracy: 1.0000 - val_loss: 0.2475 - val_accuracy: 0.8333\n",
      "Epoch 350/1000\n",
      "63/63 [==============================] - 0s 271us/step - loss: 0.0080 - accuracy: 1.0000 - val_loss: 0.2482 - val_accuracy: 0.8333\n",
      "Epoch 351/1000\n",
      "63/63 [==============================] - 0s 266us/step - loss: 0.0144 - accuracy: 1.0000 - val_loss: 0.1970 - val_accuracy: 0.8333\n",
      "Epoch 352/1000\n",
      "63/63 [==============================] - 0s 271us/step - loss: 0.0217 - accuracy: 1.0000 - val_loss: 0.2087 - val_accuracy: 0.8333\n",
      "Epoch 353/1000\n",
      "63/63 [==============================] - 0s 295us/step - loss: 0.0702 - accuracy: 0.9524 - val_loss: 0.1526 - val_accuracy: 0.9167\n",
      "Epoch 354/1000\n",
      "63/63 [==============================] - 0s 224us/step - loss: 0.0424 - accuracy: 0.9841 - val_loss: 0.1000 - val_accuracy: 1.0000\n",
      "Epoch 355/1000\n",
      "63/63 [==============================] - 0s 328us/step - loss: 0.0440 - accuracy: 0.9841 - val_loss: 0.1679 - val_accuracy: 0.8333\n",
      "Epoch 356/1000\n",
      "63/63 [==============================] - 0s 238us/step - loss: 0.0175 - accuracy: 1.0000 - val_loss: 0.1824 - val_accuracy: 0.8333\n",
      "Epoch 357/1000\n",
      "63/63 [==============================] - 0s 309us/step - loss: 0.0124 - accuracy: 1.0000 - val_loss: 0.2173 - val_accuracy: 0.8333\n",
      "Epoch 358/1000\n",
      "63/63 [==============================] - 0s 283us/step - loss: 0.0338 - accuracy: 0.9841 - val_loss: 0.1995 - val_accuracy: 0.8333\n",
      "Epoch 359/1000\n",
      "63/63 [==============================] - 0s 259us/step - loss: 0.0737 - accuracy: 0.9841 - val_loss: 0.1337 - val_accuracy: 0.9167\n",
      "Epoch 360/1000\n",
      "63/63 [==============================] - 0s 247us/step - loss: 0.0181 - accuracy: 0.9841 - val_loss: 0.1753 - val_accuracy: 0.8333\n",
      "Epoch 361/1000\n",
      "63/63 [==============================] - 0s 227us/step - loss: 0.1359 - accuracy: 0.9683 - val_loss: 0.0917 - val_accuracy: 1.0000\n",
      "Epoch 362/1000\n",
      "63/63 [==============================] - 0s 271us/step - loss: 0.0666 - accuracy: 0.9841 - val_loss: 0.0662 - val_accuracy: 1.0000\n",
      "Epoch 363/1000\n",
      "63/63 [==============================] - 0s 225us/step - loss: 0.0539 - accuracy: 0.9841 - val_loss: 0.0594 - val_accuracy: 1.0000\n",
      "Epoch 364/1000\n",
      "63/63 [==============================] - 0s 246us/step - loss: 0.0448 - accuracy: 0.9683 - val_loss: 0.0806 - val_accuracy: 1.0000\n",
      "Epoch 365/1000\n",
      "63/63 [==============================] - 0s 156us/step - loss: 0.0768 - accuracy: 0.9841 - val_loss: 0.0637 - val_accuracy: 1.0000\n",
      "Epoch 366/1000\n",
      "63/63 [==============================] - 0s 299us/step - loss: 0.0079 - accuracy: 1.0000 - val_loss: 0.0708 - val_accuracy: 1.0000\n",
      "Epoch 367/1000\n",
      "63/63 [==============================] - 0s 276us/step - loss: 0.0361 - accuracy: 0.9683 - val_loss: 0.0944 - val_accuracy: 1.0000\n",
      "Epoch 368/1000\n",
      "63/63 [==============================] - 0s 231us/step - loss: 0.0275 - accuracy: 1.0000 - val_loss: 0.0823 - val_accuracy: 1.0000\n",
      "Epoch 369/1000\n",
      "63/63 [==============================] - 0s 183us/step - loss: 0.0296 - accuracy: 1.0000 - val_loss: 0.0909 - val_accuracy: 1.0000\n",
      "Epoch 370/1000\n",
      "63/63 [==============================] - 0s 234us/step - loss: 0.0135 - accuracy: 1.0000 - val_loss: 0.1245 - val_accuracy: 0.9167\n",
      "Epoch 371/1000\n",
      "63/63 [==============================] - 0s 240us/step - loss: 0.0404 - accuracy: 0.9841 - val_loss: 0.1849 - val_accuracy: 0.8333\n",
      "Epoch 372/1000\n",
      "63/63 [==============================] - 0s 249us/step - loss: 0.0349 - accuracy: 0.9841 - val_loss: 0.2347 - val_accuracy: 0.8333\n",
      "Epoch 373/1000\n",
      "63/63 [==============================] - 0s 214us/step - loss: 0.0260 - accuracy: 0.9841 - val_loss: 0.1611 - val_accuracy: 0.9167\n",
      "Epoch 374/1000\n",
      "63/63 [==============================] - 0s 272us/step - loss: 0.0628 - accuracy: 0.9683 - val_loss: 0.0958 - val_accuracy: 1.0000\n",
      "Epoch 375/1000\n",
      "63/63 [==============================] - 0s 199us/step - loss: 0.0542 - accuracy: 0.9683 - val_loss: 0.1324 - val_accuracy: 0.9167\n",
      "Epoch 376/1000\n",
      "63/63 [==============================] - 0s 231us/step - loss: 0.0117 - accuracy: 1.0000 - val_loss: 0.1611 - val_accuracy: 0.9167\n",
      "Epoch 377/1000\n",
      "63/63 [==============================] - 0s 206us/step - loss: 0.0165 - accuracy: 1.0000 - val_loss: 0.1127 - val_accuracy: 0.9167\n",
      "Epoch 378/1000\n",
      "63/63 [==============================] - 0s 279us/step - loss: 0.0334 - accuracy: 0.9841 - val_loss: 0.1683 - val_accuracy: 0.8333\n",
      "Epoch 379/1000\n",
      "63/63 [==============================] - 0s 256us/step - loss: 0.0127 - accuracy: 1.0000 - val_loss: 0.1577 - val_accuracy: 0.9167\n",
      "Epoch 380/1000\n",
      "63/63 [==============================] - 0s 280us/step - loss: 0.0462 - accuracy: 0.9841 - val_loss: 0.1857 - val_accuracy: 0.8333\n",
      "Epoch 381/1000\n",
      "63/63 [==============================] - 0s 232us/step - loss: 0.0964 - accuracy: 0.9841 - val_loss: 0.1601 - val_accuracy: 0.9167\n",
      "Epoch 382/1000\n",
      "63/63 [==============================] - 0s 283us/step - loss: 0.0388 - accuracy: 0.9841 - val_loss: 0.1383 - val_accuracy: 0.9167\n",
      "Epoch 383/1000\n",
      "63/63 [==============================] - 0s 246us/step - loss: 0.0746 - accuracy: 0.9683 - val_loss: 0.1418 - val_accuracy: 0.9167\n",
      "Epoch 384/1000\n",
      "63/63 [==============================] - 0s 254us/step - loss: 0.0205 - accuracy: 1.0000 - val_loss: 0.1009 - val_accuracy: 1.0000\n",
      "Epoch 385/1000\n",
      "63/63 [==============================] - 0s 255us/step - loss: 0.1345 - accuracy: 0.9683 - val_loss: 0.0933 - val_accuracy: 1.0000\n",
      "Epoch 386/1000\n",
      "63/63 [==============================] - 0s 223us/step - loss: 0.0206 - accuracy: 1.0000 - val_loss: 0.1316 - val_accuracy: 0.9167\n",
      "Epoch 387/1000\n",
      "63/63 [==============================] - 0s 199us/step - loss: 0.0135 - accuracy: 1.0000 - val_loss: 0.1150 - val_accuracy: 0.9167\n",
      "Epoch 388/1000\n",
      "63/63 [==============================] - 0s 239us/step - loss: 0.0868 - accuracy: 0.9841 - val_loss: 0.0916 - val_accuracy: 1.0000\n",
      "Epoch 389/1000\n",
      "63/63 [==============================] - 0s 203us/step - loss: 0.0960 - accuracy: 0.9841 - val_loss: 0.0956 - val_accuracy: 1.0000\n",
      "Epoch 390/1000\n",
      "63/63 [==============================] - 0s 315us/step - loss: 0.0316 - accuracy: 1.0000 - val_loss: 0.1039 - val_accuracy: 1.0000\n",
      "Epoch 391/1000\n",
      "63/63 [==============================] - 0s 194us/step - loss: 0.0174 - accuracy: 1.0000 - val_loss: 0.1317 - val_accuracy: 0.9167\n",
      "Epoch 392/1000\n",
      "63/63 [==============================] - 0s 230us/step - loss: 0.0285 - accuracy: 0.9841 - val_loss: 0.1809 - val_accuracy: 0.8333\n",
      "Epoch 393/1000\n",
      "63/63 [==============================] - 0s 185us/step - loss: 0.0141 - accuracy: 1.0000 - val_loss: 0.1910 - val_accuracy: 0.8333\n",
      "Epoch 394/1000\n",
      "63/63 [==============================] - 0s 256us/step - loss: 0.0128 - accuracy: 1.0000 - val_loss: 0.1954 - val_accuracy: 0.8333\n",
      "Epoch 395/1000\n",
      "63/63 [==============================] - 0s 164us/step - loss: 0.0107 - accuracy: 1.0000 - val_loss: 0.1945 - val_accuracy: 0.8333\n",
      "Epoch 396/1000\n",
      "63/63 [==============================] - 0s 276us/step - loss: 0.0132 - accuracy: 1.0000 - val_loss: 0.1659 - val_accuracy: 0.8333\n",
      "Epoch 397/1000\n",
      "63/63 [==============================] - 0s 247us/step - loss: 0.0858 - accuracy: 0.9524 - val_loss: 0.1517 - val_accuracy: 0.9167\n",
      "Epoch 398/1000\n",
      "63/63 [==============================] - 0s 264us/step - loss: 0.0886 - accuracy: 0.9841 - val_loss: 0.1898 - val_accuracy: 0.8333\n",
      "Epoch 399/1000\n",
      "63/63 [==============================] - 0s 169us/step - loss: 0.0301 - accuracy: 0.9841 - val_loss: 0.2516 - val_accuracy: 0.8333\n",
      "Epoch 400/1000\n",
      "63/63 [==============================] - 0s 249us/step - loss: 0.0084 - accuracy: 1.0000 - val_loss: 0.2585 - val_accuracy: 0.8333\n",
      "Epoch 401/1000\n",
      "63/63 [==============================] - 0s 231us/step - loss: 0.0191 - accuracy: 1.0000 - val_loss: 0.2044 - val_accuracy: 0.8333\n",
      "Epoch 402/1000\n",
      "63/63 [==============================] - 0s 218us/step - loss: 0.0113 - accuracy: 1.0000 - val_loss: 0.1885 - val_accuracy: 0.8333\n",
      "Epoch 403/1000\n",
      "63/63 [==============================] - 0s 229us/step - loss: 0.0207 - accuracy: 1.0000 - val_loss: 0.1968 - val_accuracy: 0.8333\n",
      "Epoch 404/1000\n",
      "63/63 [==============================] - 0s 255us/step - loss: 0.0532 - accuracy: 0.9841 - val_loss: 0.1380 - val_accuracy: 0.9167\n",
      "Epoch 405/1000\n",
      "63/63 [==============================] - 0s 186us/step - loss: 0.0186 - accuracy: 0.9841 - val_loss: 0.1053 - val_accuracy: 1.0000\n",
      "Epoch 406/1000\n",
      "63/63 [==============================] - 0s 287us/step - loss: 0.0298 - accuracy: 1.0000 - val_loss: 0.1493 - val_accuracy: 0.9167\n",
      "Epoch 407/1000\n",
      "63/63 [==============================] - 0s 215us/step - loss: 0.0123 - accuracy: 1.0000 - val_loss: 0.1587 - val_accuracy: 0.9167\n",
      "Epoch 408/1000\n",
      "63/63 [==============================] - 0s 245us/step - loss: 0.0824 - accuracy: 0.9841 - val_loss: 0.1027 - val_accuracy: 1.0000\n",
      "Epoch 409/1000\n",
      "63/63 [==============================] - 0s 224us/step - loss: 0.0096 - accuracy: 1.0000 - val_loss: 0.1081 - val_accuracy: 0.9167\n",
      "Epoch 410/1000\n",
      "63/63 [==============================] - 0s 255us/step - loss: 0.0509 - accuracy: 0.9683 - val_loss: 0.1600 - val_accuracy: 0.9167\n",
      "Epoch 411/1000\n",
      "63/63 [==============================] - 0s 201us/step - loss: 0.0981 - accuracy: 0.9841 - val_loss: 0.1021 - val_accuracy: 1.0000\n",
      "Epoch 412/1000\n",
      "63/63 [==============================] - 0s 216us/step - loss: 0.0604 - accuracy: 0.9683 - val_loss: 0.0813 - val_accuracy: 1.0000\n",
      "Epoch 413/1000\n",
      "63/63 [==============================] - 0s 281us/step - loss: 0.0098 - accuracy: 1.0000 - val_loss: 0.0923 - val_accuracy: 1.0000\n",
      "Epoch 414/1000\n",
      "63/63 [==============================] - 0s 255us/step - loss: 0.0594 - accuracy: 0.9841 - val_loss: 0.1604 - val_accuracy: 0.9167\n",
      "Epoch 415/1000\n",
      "63/63 [==============================] - 0s 183us/step - loss: 0.0114 - accuracy: 1.0000 - val_loss: 0.1497 - val_accuracy: 0.9167\n",
      "Epoch 416/1000\n",
      "63/63 [==============================] - 0s 237us/step - loss: 0.0071 - accuracy: 1.0000 - val_loss: 0.1611 - val_accuracy: 0.9167\n",
      "Epoch 417/1000\n",
      "63/63 [==============================] - 0s 261us/step - loss: 0.0340 - accuracy: 0.9841 - val_loss: 0.1321 - val_accuracy: 0.9167\n",
      "Epoch 418/1000\n",
      "63/63 [==============================] - 0s 237us/step - loss: 0.0535 - accuracy: 0.9841 - val_loss: 0.0852 - val_accuracy: 1.0000\n",
      "Epoch 419/1000\n",
      "63/63 [==============================] - 0s 206us/step - loss: 0.0225 - accuracy: 1.0000 - val_loss: 0.1070 - val_accuracy: 0.9167\n",
      "Epoch 420/1000\n",
      "63/63 [==============================] - 0s 214us/step - loss: 0.0372 - accuracy: 0.9841 - val_loss: 0.0991 - val_accuracy: 1.0000\n",
      "Epoch 421/1000\n",
      "63/63 [==============================] - 0s 256us/step - loss: 0.0153 - accuracy: 1.0000 - val_loss: 0.1306 - val_accuracy: 0.9167\n",
      "Epoch 422/1000\n",
      "63/63 [==============================] - 0s 205us/step - loss: 0.0216 - accuracy: 0.9841 - val_loss: 0.1749 - val_accuracy: 0.8333\n",
      "Epoch 423/1000\n",
      "63/63 [==============================] - 0s 248us/step - loss: 0.0572 - accuracy: 0.9524 - val_loss: 0.1195 - val_accuracy: 0.9167\n",
      "Epoch 424/1000\n",
      "63/63 [==============================] - 0s 183us/step - loss: 0.0085 - accuracy: 1.0000 - val_loss: 0.1369 - val_accuracy: 0.9167\n",
      "Epoch 425/1000\n",
      "63/63 [==============================] - 0s 247us/step - loss: 0.0268 - accuracy: 1.0000 - val_loss: 0.1389 - val_accuracy: 0.9167\n",
      "Epoch 426/1000\n",
      "63/63 [==============================] - 0s 213us/step - loss: 0.0083 - accuracy: 1.0000 - val_loss: 0.1276 - val_accuracy: 0.9167\n",
      "Epoch 427/1000\n",
      "63/63 [==============================] - 0s 335us/step - loss: 0.0050 - accuracy: 1.0000 - val_loss: 0.1316 - val_accuracy: 0.9167\n",
      "Epoch 428/1000\n",
      "63/63 [==============================] - 0s 271us/step - loss: 0.0347 - accuracy: 0.9683 - val_loss: 0.1367 - val_accuracy: 0.9167\n",
      "Epoch 429/1000\n",
      "63/63 [==============================] - 0s 249us/step - loss: 0.0210 - accuracy: 1.0000 - val_loss: 0.1241 - val_accuracy: 0.9167\n",
      "Epoch 430/1000\n",
      "63/63 [==============================] - 0s 255us/step - loss: 0.0143 - accuracy: 1.0000 - val_loss: 0.1068 - val_accuracy: 0.9167\n",
      "Epoch 431/1000\n",
      "63/63 [==============================] - 0s 223us/step - loss: 0.0204 - accuracy: 0.9841 - val_loss: 0.1430 - val_accuracy: 0.9167\n",
      "Epoch 432/1000\n",
      "63/63 [==============================] - 0s 199us/step - loss: 0.0477 - accuracy: 0.9841 - val_loss: 0.1301 - val_accuracy: 0.9167\n",
      "Epoch 433/1000\n",
      "63/63 [==============================] - 0s 259us/step - loss: 0.0549 - accuracy: 0.9683 - val_loss: 0.1475 - val_accuracy: 0.9167\n",
      "Epoch 434/1000\n",
      "63/63 [==============================] - 0s 303us/step - loss: 0.0681 - accuracy: 0.9841 - val_loss: 0.1815 - val_accuracy: 0.8333\n",
      "Epoch 435/1000\n",
      "63/63 [==============================] - 0s 195us/step - loss: 0.0634 - accuracy: 0.9683 - val_loss: 0.0985 - val_accuracy: 1.0000\n",
      "Epoch 436/1000\n",
      "63/63 [==============================] - 0s 218us/step - loss: 0.0270 - accuracy: 1.0000 - val_loss: 0.1048 - val_accuracy: 1.0000\n",
      "Epoch 437/1000\n",
      "63/63 [==============================] - 0s 224us/step - loss: 0.0068 - accuracy: 1.0000 - val_loss: 0.1066 - val_accuracy: 0.9167\n",
      "Epoch 438/1000\n",
      "63/63 [==============================] - 0s 183us/step - loss: 0.0568 - accuracy: 0.9841 - val_loss: 0.0838 - val_accuracy: 1.0000\n",
      "Epoch 439/1000\n",
      "63/63 [==============================] - 0s 259us/step - loss: 0.0136 - accuracy: 1.0000 - val_loss: 0.0875 - val_accuracy: 1.0000\n",
      "Epoch 440/1000\n",
      "63/63 [==============================] - 0s 200us/step - loss: 0.0118 - accuracy: 1.0000 - val_loss: 0.1065 - val_accuracy: 0.9167\n",
      "Epoch 441/1000\n",
      "63/63 [==============================] - 0s 278us/step - loss: 0.0224 - accuracy: 1.0000 - val_loss: 0.0918 - val_accuracy: 1.0000\n",
      "Epoch 442/1000\n",
      "63/63 [==============================] - 0s 216us/step - loss: 0.0687 - accuracy: 0.9841 - val_loss: 0.0887 - val_accuracy: 1.0000\n",
      "Epoch 443/1000\n",
      "63/63 [==============================] - 0s 272us/step - loss: 0.0350 - accuracy: 0.9841 - val_loss: 0.1256 - val_accuracy: 0.9167\n",
      "Epoch 444/1000\n",
      "63/63 [==============================] - 0s 234us/step - loss: 0.0286 - accuracy: 0.9841 - val_loss: 0.1243 - val_accuracy: 0.9167\n",
      "Epoch 445/1000\n",
      "63/63 [==============================] - 0s 272us/step - loss: 0.0122 - accuracy: 1.0000 - val_loss: 0.1192 - val_accuracy: 0.9167\n",
      "Epoch 446/1000\n",
      "63/63 [==============================] - 0s 209us/step - loss: 0.0092 - accuracy: 1.0000 - val_loss: 0.1296 - val_accuracy: 0.9167\n",
      "Epoch 447/1000\n",
      "63/63 [==============================] - 0s 229us/step - loss: 0.0144 - accuracy: 1.0000 - val_loss: 0.1435 - val_accuracy: 0.9167\n",
      "Epoch 448/1000\n",
      "63/63 [==============================] - 0s 208us/step - loss: 0.0115 - accuracy: 1.0000 - val_loss: 0.1621 - val_accuracy: 0.9167\n",
      "Epoch 449/1000\n",
      "63/63 [==============================] - 0s 257us/step - loss: 0.1081 - accuracy: 0.9683 - val_loss: 0.0960 - val_accuracy: 1.0000\n",
      "Epoch 450/1000\n",
      "63/63 [==============================] - 0s 559us/step - loss: 0.0441 - accuracy: 0.9841 - val_loss: 0.0805 - val_accuracy: 1.0000\n",
      "Epoch 451/1000\n",
      "63/63 [==============================] - 0s 384us/step - loss: 0.0345 - accuracy: 0.9841 - val_loss: 0.1372 - val_accuracy: 0.9167\n",
      "Epoch 452/1000\n",
      "63/63 [==============================] - 0s 215us/step - loss: 0.1191 - accuracy: 0.9841 - val_loss: 0.1517 - val_accuracy: 0.9167\n",
      "Epoch 453/1000\n",
      "63/63 [==============================] - 0s 283us/step - loss: 0.0901 - accuracy: 0.9683 - val_loss: 0.1259 - val_accuracy: 0.9167\n",
      "Epoch 454/1000\n",
      "63/63 [==============================] - 0s 200us/step - loss: 0.0157 - accuracy: 1.0000 - val_loss: 0.1479 - val_accuracy: 0.9167\n",
      "Epoch 455/1000\n",
      "63/63 [==============================] - 0s 240us/step - loss: 0.0185 - accuracy: 1.0000 - val_loss: 0.1819 - val_accuracy: 0.8333\n",
      "Epoch 456/1000\n",
      "63/63 [==============================] - 0s 178us/step - loss: 0.0284 - accuracy: 0.9841 - val_loss: 0.1640 - val_accuracy: 0.9167\n",
      "Epoch 457/1000\n",
      "63/63 [==============================] - 0s 247us/step - loss: 0.0098 - accuracy: 1.0000 - val_loss: 0.1655 - val_accuracy: 0.9167\n",
      "Epoch 458/1000\n",
      "63/63 [==============================] - 0s 199us/step - loss: 0.0158 - accuracy: 1.0000 - val_loss: 0.1831 - val_accuracy: 0.8333\n",
      "Epoch 459/1000\n",
      "63/63 [==============================] - 0s 293us/step - loss: 0.0181 - accuracy: 1.0000 - val_loss: 0.1988 - val_accuracy: 0.8333\n",
      "Epoch 460/1000\n",
      "63/63 [==============================] - 0s 177us/step - loss: 0.0136 - accuracy: 1.0000 - val_loss: 0.1544 - val_accuracy: 0.9167\n",
      "Epoch 461/1000\n",
      "63/63 [==============================] - 0s 225us/step - loss: 0.0949 - accuracy: 0.9841 - val_loss: 0.1694 - val_accuracy: 0.8333\n",
      "Epoch 462/1000\n",
      "63/63 [==============================] - 0s 215us/step - loss: 0.0350 - accuracy: 0.9683 - val_loss: 0.1335 - val_accuracy: 0.9167\n",
      "Epoch 463/1000\n",
      "63/63 [==============================] - 0s 250us/step - loss: 0.0234 - accuracy: 0.9841 - val_loss: 0.1578 - val_accuracy: 0.9167\n",
      "Epoch 464/1000\n",
      "63/63 [==============================] - 0s 238us/step - loss: 0.0379 - accuracy: 0.9841 - val_loss: 0.1301 - val_accuracy: 0.9167\n",
      "Epoch 465/1000\n",
      "63/63 [==============================] - 0s 304us/step - loss: 0.0254 - accuracy: 0.9841 - val_loss: 0.1749 - val_accuracy: 0.8333\n",
      "Epoch 466/1000\n",
      "63/63 [==============================] - 0s 193us/step - loss: 0.1135 - accuracy: 0.9841 - val_loss: 0.1643 - val_accuracy: 0.9167\n",
      "Epoch 467/1000\n",
      "63/63 [==============================] - 0s 212us/step - loss: 0.0091 - accuracy: 1.0000 - val_loss: 0.1626 - val_accuracy: 0.9167\n",
      "Epoch 468/1000\n",
      "63/63 [==============================] - 0s 208us/step - loss: 0.0727 - accuracy: 0.9841 - val_loss: 0.0996 - val_accuracy: 1.0000\n",
      "Epoch 469/1000\n",
      "63/63 [==============================] - 0s 278us/step - loss: 0.0189 - accuracy: 1.0000 - val_loss: 0.1360 - val_accuracy: 0.9167\n",
      "Epoch 470/1000\n",
      "63/63 [==============================] - 0s 267us/step - loss: 0.0519 - accuracy: 0.9683 - val_loss: 0.0960 - val_accuracy: 1.0000\n",
      "Epoch 471/1000\n",
      "63/63 [==============================] - 0s 209us/step - loss: 0.0079 - accuracy: 1.0000 - val_loss: 0.1061 - val_accuracy: 0.9167\n",
      "Epoch 472/1000\n",
      "63/63 [==============================] - 0s 247us/step - loss: 0.0297 - accuracy: 0.9841 - val_loss: 0.1547 - val_accuracy: 0.9167\n",
      "Epoch 473/1000\n",
      "63/63 [==============================] - 0s 264us/step - loss: 0.0280 - accuracy: 0.9841 - val_loss: 0.1391 - val_accuracy: 0.9167\n",
      "Epoch 474/1000\n",
      "63/63 [==============================] - 0s 228us/step - loss: 0.0281 - accuracy: 0.9841 - val_loss: 0.1696 - val_accuracy: 0.8333\n",
      "Epoch 475/1000\n",
      "63/63 [==============================] - 0s 256us/step - loss: 0.0058 - accuracy: 1.0000 - val_loss: 0.1747 - val_accuracy: 0.8333\n",
      "Epoch 476/1000\n",
      "63/63 [==============================] - 0s 200us/step - loss: 0.0149 - accuracy: 1.0000 - val_loss: 0.1456 - val_accuracy: 0.9167\n",
      "Epoch 477/1000\n",
      "63/63 [==============================] - 0s 263us/step - loss: 0.0149 - accuracy: 1.0000 - val_loss: 0.1449 - val_accuracy: 0.9167\n",
      "Epoch 478/1000\n",
      "63/63 [==============================] - 0s 175us/step - loss: 0.0299 - accuracy: 0.9841 - val_loss: 0.1534 - val_accuracy: 0.9167\n",
      "Epoch 479/1000\n",
      "63/63 [==============================] - 0s 244us/step - loss: 0.0402 - accuracy: 0.9683 - val_loss: 0.0966 - val_accuracy: 1.0000\n",
      "Epoch 480/1000\n",
      "63/63 [==============================] - 0s 257us/step - loss: 0.0099 - accuracy: 1.0000 - val_loss: 0.1033 - val_accuracy: 1.0000\n",
      "Epoch 481/1000\n",
      "63/63 [==============================] - 0s 264us/step - loss: 0.0301 - accuracy: 0.9841 - val_loss: 0.0834 - val_accuracy: 1.0000\n",
      "Epoch 482/1000\n",
      "63/63 [==============================] - 0s 235us/step - loss: 0.0272 - accuracy: 1.0000 - val_loss: 0.0932 - val_accuracy: 1.0000\n",
      "Epoch 483/1000\n",
      "63/63 [==============================] - 0s 282us/step - loss: 0.0740 - accuracy: 0.9683 - val_loss: 0.0752 - val_accuracy: 1.0000\n",
      "Epoch 484/1000\n",
      "63/63 [==============================] - 0s 303us/step - loss: 0.0553 - accuracy: 0.9841 - val_loss: 0.0710 - val_accuracy: 1.0000\n",
      "Epoch 485/1000\n",
      "63/63 [==============================] - 0s 288us/step - loss: 0.0325 - accuracy: 0.9841 - val_loss: 0.0610 - val_accuracy: 1.0000\n",
      "Epoch 486/1000\n",
      "63/63 [==============================] - 0s 406us/step - loss: 0.0146 - accuracy: 1.0000 - val_loss: 0.0769 - val_accuracy: 1.0000\n",
      "Epoch 487/1000\n",
      "63/63 [==============================] - 0s 215us/step - loss: 0.0302 - accuracy: 0.9841 - val_loss: 0.1124 - val_accuracy: 0.9167\n",
      "Epoch 488/1000\n",
      "63/63 [==============================] - 0s 280us/step - loss: 0.0404 - accuracy: 0.9683 - val_loss: 0.1172 - val_accuracy: 0.9167\n",
      "Epoch 489/1000\n",
      "63/63 [==============================] - 0s 198us/step - loss: 0.0122 - accuracy: 1.0000 - val_loss: 0.1408 - val_accuracy: 0.9167\n",
      "Epoch 490/1000\n",
      "63/63 [==============================] - 0s 335us/step - loss: 0.0185 - accuracy: 1.0000 - val_loss: 0.1304 - val_accuracy: 0.9167\n",
      "Epoch 491/1000\n",
      "63/63 [==============================] - 0s 272us/step - loss: 0.0139 - accuracy: 1.0000 - val_loss: 0.1458 - val_accuracy: 0.9167\n",
      "Epoch 492/1000\n",
      "63/63 [==============================] - 0s 216us/step - loss: 0.0714 - accuracy: 0.9841 - val_loss: 0.0903 - val_accuracy: 1.0000\n",
      "Epoch 493/1000\n",
      "63/63 [==============================] - 0s 212us/step - loss: 0.0280 - accuracy: 0.9841 - val_loss: 0.0619 - val_accuracy: 1.0000\n",
      "Epoch 494/1000\n",
      "63/63 [==============================] - 0s 231us/step - loss: 0.0282 - accuracy: 0.9841 - val_loss: 0.0903 - val_accuracy: 1.0000\n",
      "Epoch 495/1000\n",
      "63/63 [==============================] - 0s 263us/step - loss: 0.0130 - accuracy: 1.0000 - val_loss: 0.0950 - val_accuracy: 1.0000\n",
      "Epoch 496/1000\n",
      "63/63 [==============================] - 0s 239us/step - loss: 0.0209 - accuracy: 1.0000 - val_loss: 0.0766 - val_accuracy: 1.0000\n",
      "Epoch 497/1000\n",
      "63/63 [==============================] - 0s 215us/step - loss: 0.0122 - accuracy: 1.0000 - val_loss: 0.0963 - val_accuracy: 1.0000\n",
      "Epoch 498/1000\n",
      "63/63 [==============================] - 0s 239us/step - loss: 0.0424 - accuracy: 0.9841 - val_loss: 0.0863 - val_accuracy: 1.0000\n",
      "Epoch 499/1000\n",
      "63/63 [==============================] - 0s 255us/step - loss: 0.0152 - accuracy: 1.0000 - val_loss: 0.0956 - val_accuracy: 1.0000\n",
      "Epoch 500/1000\n",
      "63/63 [==============================] - 0s 241us/step - loss: 0.0164 - accuracy: 1.0000 - val_loss: 0.1049 - val_accuracy: 0.9167\n",
      "Epoch 501/1000\n",
      "63/63 [==============================] - 0s 215us/step - loss: 0.0346 - accuracy: 0.9841 - val_loss: 0.0897 - val_accuracy: 1.0000\n",
      "Epoch 502/1000\n",
      "63/63 [==============================] - 0s 258us/step - loss: 0.0094 - accuracy: 1.0000 - val_loss: 0.1147 - val_accuracy: 0.9167\n",
      "Epoch 503/1000\n",
      "63/63 [==============================] - 0s 353us/step - loss: 0.0107 - accuracy: 1.0000 - val_loss: 0.1249 - val_accuracy: 0.9167\n",
      "Epoch 504/1000\n",
      "63/63 [==============================] - 0s 273us/step - loss: 0.0804 - accuracy: 0.9683 - val_loss: 0.1182 - val_accuracy: 0.9167\n",
      "Epoch 505/1000\n",
      "63/63 [==============================] - 0s 265us/step - loss: 0.0466 - accuracy: 0.9841 - val_loss: 0.0999 - val_accuracy: 1.0000\n",
      "Epoch 506/1000\n",
      "63/63 [==============================] - 0s 200us/step - loss: 0.0164 - accuracy: 1.0000 - val_loss: 0.1142 - val_accuracy: 0.9167\n",
      "Epoch 507/1000\n",
      "63/63 [==============================] - 0s 236us/step - loss: 0.1294 - accuracy: 0.9683 - val_loss: 0.0863 - val_accuracy: 1.0000\n",
      "Epoch 508/1000\n",
      "63/63 [==============================] - 0s 209us/step - loss: 0.0169 - accuracy: 1.0000 - val_loss: 0.1078 - val_accuracy: 0.9167\n",
      "Epoch 509/1000\n",
      "63/63 [==============================] - 0s 288us/step - loss: 0.0172 - accuracy: 1.0000 - val_loss: 0.1015 - val_accuracy: 1.0000\n",
      "Epoch 510/1000\n",
      "63/63 [==============================] - 0s 189us/step - loss: 0.0311 - accuracy: 0.9841 - val_loss: 0.1143 - val_accuracy: 0.9167\n",
      "Epoch 511/1000\n",
      "63/63 [==============================] - 0s 246us/step - loss: 0.0961 - accuracy: 0.9841 - val_loss: 0.1182 - val_accuracy: 0.9167\n",
      "Epoch 512/1000\n",
      "63/63 [==============================] - 0s 202us/step - loss: 0.0694 - accuracy: 0.9841 - val_loss: 0.1037 - val_accuracy: 1.0000\n",
      "Epoch 513/1000\n",
      "63/63 [==============================] - 0s 286us/step - loss: 0.0974 - accuracy: 0.9841 - val_loss: 0.0973 - val_accuracy: 1.0000\n",
      "Epoch 514/1000\n",
      "63/63 [==============================] - 0s 263us/step - loss: 0.0123 - accuracy: 1.0000 - val_loss: 0.1089 - val_accuracy: 0.9167\n",
      "Epoch 515/1000\n",
      "63/63 [==============================] - 0s 240us/step - loss: 0.0166 - accuracy: 1.0000 - val_loss: 0.1439 - val_accuracy: 0.9167\n",
      "Epoch 516/1000\n",
      "63/63 [==============================] - 0s 188us/step - loss: 0.0175 - accuracy: 1.0000 - val_loss: 0.1534 - val_accuracy: 0.9167\n",
      "Epoch 517/1000\n",
      "63/63 [==============================] - 0s 265us/step - loss: 0.0138 - accuracy: 1.0000 - val_loss: 0.1486 - val_accuracy: 0.9167\n",
      "Epoch 518/1000\n",
      "63/63 [==============================] - 0s 231us/step - loss: 0.0152 - accuracy: 1.0000 - val_loss: 0.1579 - val_accuracy: 0.9167\n",
      "Epoch 519/1000\n",
      "63/63 [==============================] - 0s 263us/step - loss: 0.0338 - accuracy: 0.9841 - val_loss: 0.1449 - val_accuracy: 0.9167\n",
      "Epoch 520/1000\n",
      "63/63 [==============================] - 0s 192us/step - loss: 0.0891 - accuracy: 0.9841 - val_loss: 0.1582 - val_accuracy: 0.9167\n",
      "Epoch 521/1000\n",
      "63/63 [==============================] - 0s 248us/step - loss: 0.0169 - accuracy: 1.0000 - val_loss: 0.1493 - val_accuracy: 0.9167\n",
      "Epoch 522/1000\n",
      "63/63 [==============================] - 0s 202us/step - loss: 0.0136 - accuracy: 1.0000 - val_loss: 0.1685 - val_accuracy: 0.8333\n",
      "Epoch 523/1000\n",
      "63/63 [==============================] - 0s 221us/step - loss: 0.0159 - accuracy: 1.0000 - val_loss: 0.1673 - val_accuracy: 0.8333\n",
      "Epoch 524/1000\n",
      "63/63 [==============================] - 0s 241us/step - loss: 0.0079 - accuracy: 1.0000 - val_loss: 0.1607 - val_accuracy: 0.9167\n",
      "Epoch 525/1000\n",
      "63/63 [==============================] - 0s 259us/step - loss: 0.0283 - accuracy: 0.9841 - val_loss: 0.0972 - val_accuracy: 1.0000\n",
      "Epoch 526/1000\n",
      "63/63 [==============================] - 0s 247us/step - loss: 0.0499 - accuracy: 0.9524 - val_loss: 0.0828 - val_accuracy: 1.0000\n",
      "Epoch 527/1000\n",
      "63/63 [==============================] - 0s 247us/step - loss: 0.0109 - accuracy: 1.0000 - val_loss: 0.0927 - val_accuracy: 1.0000\n",
      "Epoch 528/1000\n",
      "63/63 [==============================] - 0s 199us/step - loss: 0.0112 - accuracy: 1.0000 - val_loss: 0.1103 - val_accuracy: 0.9167\n",
      "Epoch 529/1000\n",
      "63/63 [==============================] - 0s 247us/step - loss: 0.0312 - accuracy: 0.9841 - val_loss: 0.1206 - val_accuracy: 0.9167\n",
      "Epoch 530/1000\n",
      "63/63 [==============================] - 0s 155us/step - loss: 0.0210 - accuracy: 1.0000 - val_loss: 0.0917 - val_accuracy: 1.0000\n",
      "Epoch 531/1000\n",
      "63/63 [==============================] - 0s 247us/step - loss: 0.0341 - accuracy: 0.9841 - val_loss: 0.1368 - val_accuracy: 0.9167\n",
      "Epoch 532/1000\n",
      "63/63 [==============================] - 0s 209us/step - loss: 0.0120 - accuracy: 1.0000 - val_loss: 0.1452 - val_accuracy: 0.9167\n",
      "Epoch 533/1000\n",
      "63/63 [==============================] - 0s 199us/step - loss: 0.1030 - accuracy: 0.9683 - val_loss: 0.0746 - val_accuracy: 1.0000\n",
      "Epoch 534/1000\n",
      "63/63 [==============================] - 0s 279us/step - loss: 0.0183 - accuracy: 1.0000 - val_loss: 0.0951 - val_accuracy: 1.0000\n",
      "Epoch 535/1000\n",
      "63/63 [==============================] - 0s 281us/step - loss: 0.0081 - accuracy: 1.0000 - val_loss: 0.1053 - val_accuracy: 0.9167\n",
      "Epoch 536/1000\n",
      "63/63 [==============================] - 0s 211us/step - loss: 0.0085 - accuracy: 1.0000 - val_loss: 0.1103 - val_accuracy: 0.9167\n",
      "Epoch 537/1000\n",
      "63/63 [==============================] - 0s 236us/step - loss: 0.0072 - accuracy: 1.0000 - val_loss: 0.1207 - val_accuracy: 0.9167\n",
      "Epoch 538/1000\n",
      "63/63 [==============================] - 0s 265us/step - loss: 0.0114 - accuracy: 1.0000 - val_loss: 0.1331 - val_accuracy: 0.9167\n",
      "Epoch 539/1000\n",
      "63/63 [==============================] - 0s 279us/step - loss: 0.0146 - accuracy: 1.0000 - val_loss: 0.1668 - val_accuracy: 0.9167\n",
      "Epoch 540/1000\n",
      "63/63 [==============================] - 0s 193us/step - loss: 0.0130 - accuracy: 1.0000 - val_loss: 0.1403 - val_accuracy: 0.9167\n",
      "Epoch 541/1000\n",
      "63/63 [==============================] - 0s 215us/step - loss: 0.0094 - accuracy: 1.0000 - val_loss: 0.1428 - val_accuracy: 0.9167\n",
      "Epoch 542/1000\n",
      "63/63 [==============================] - 0s 224us/step - loss: 0.0185 - accuracy: 1.0000 - val_loss: 0.1943 - val_accuracy: 0.8333\n",
      "Epoch 543/1000\n",
      "63/63 [==============================] - 0s 319us/step - loss: 0.0419 - accuracy: 0.9841 - val_loss: 0.1398 - val_accuracy: 0.9167\n",
      "Epoch 544/1000\n",
      "63/63 [==============================] - 0s 470us/step - loss: 0.0502 - accuracy: 0.9841 - val_loss: 0.1389 - val_accuracy: 0.9167\n",
      "Epoch 545/1000\n",
      "63/63 [==============================] - 0s 319us/step - loss: 0.0354 - accuracy: 0.9841 - val_loss: 0.0994 - val_accuracy: 1.0000\n",
      "Epoch 546/1000\n",
      "63/63 [==============================] - 0s 223us/step - loss: 0.0132 - accuracy: 1.0000 - val_loss: 0.1211 - val_accuracy: 0.9167\n",
      "Epoch 547/1000\n",
      "63/63 [==============================] - 0s 295us/step - loss: 0.0611 - accuracy: 0.9841 - val_loss: 0.1089 - val_accuracy: 0.9167\n",
      "Epoch 548/1000\n",
      "63/63 [==============================] - 0s 219us/step - loss: 0.0158 - accuracy: 1.0000 - val_loss: 0.1367 - val_accuracy: 0.9167\n",
      "Epoch 549/1000\n",
      "63/63 [==============================] - 0s 239us/step - loss: 0.0245 - accuracy: 0.9841 - val_loss: 0.1951 - val_accuracy: 0.8333\n",
      "Epoch 550/1000\n",
      "63/63 [==============================] - 0s 216us/step - loss: 0.0296 - accuracy: 0.9841 - val_loss: 0.1435 - val_accuracy: 0.9167\n",
      "Epoch 551/1000\n",
      "63/63 [==============================] - 0s 224us/step - loss: 0.0336 - accuracy: 0.9841 - val_loss: 0.1415 - val_accuracy: 0.9167\n",
      "Epoch 552/1000\n",
      "63/63 [==============================] - 0s 301us/step - loss: 0.0082 - accuracy: 1.0000 - val_loss: 0.1354 - val_accuracy: 0.9167\n",
      "Epoch 553/1000\n",
      "63/63 [==============================] - 0s 263us/step - loss: 0.0111 - accuracy: 1.0000 - val_loss: 0.1349 - val_accuracy: 0.9167\n",
      "Epoch 554/1000\n",
      "63/63 [==============================] - 0s 215us/step - loss: 0.0087 - accuracy: 1.0000 - val_loss: 0.1268 - val_accuracy: 0.9167\n",
      "Epoch 555/1000\n",
      "63/63 [==============================] - 0s 287us/step - loss: 0.0203 - accuracy: 0.9841 - val_loss: 0.1043 - val_accuracy: 0.9167\n",
      "Epoch 556/1000\n",
      "63/63 [==============================] - 0s 220us/step - loss: 0.0800 - accuracy: 0.9841 - val_loss: 0.0905 - val_accuracy: 1.0000\n",
      "Epoch 557/1000\n",
      "63/63 [==============================] - 0s 244us/step - loss: 0.0138 - accuracy: 1.0000 - val_loss: 0.1152 - val_accuracy: 0.9167\n",
      "Epoch 558/1000\n",
      "63/63 [==============================] - 0s 210us/step - loss: 0.0158 - accuracy: 1.0000 - val_loss: 0.1115 - val_accuracy: 0.9167\n",
      "Epoch 559/1000\n",
      "63/63 [==============================] - 0s 316us/step - loss: 0.0097 - accuracy: 1.0000 - val_loss: 0.1263 - val_accuracy: 0.9167\n",
      "Epoch 560/1000\n",
      "63/63 [==============================] - 0s 203us/step - loss: 0.0168 - accuracy: 1.0000 - val_loss: 0.1016 - val_accuracy: 0.9167\n",
      "Epoch 561/1000\n",
      "63/63 [==============================] - 0s 290us/step - loss: 0.0212 - accuracy: 0.9841 - val_loss: 0.1461 - val_accuracy: 0.9167\n",
      "Epoch 562/1000\n",
      "63/63 [==============================] - 0s 183us/step - loss: 0.0381 - accuracy: 0.9841 - val_loss: 0.1747 - val_accuracy: 0.8333\n",
      "Epoch 563/1000\n",
      "63/63 [==============================] - 0s 255us/step - loss: 0.0378 - accuracy: 0.9683 - val_loss: 0.1064 - val_accuracy: 0.9167\n",
      "Epoch 564/1000\n",
      "63/63 [==============================] - 0s 231us/step - loss: 0.0102 - accuracy: 1.0000 - val_loss: 0.1248 - val_accuracy: 0.9167\n",
      "Epoch 565/1000\n",
      "63/63 [==============================] - 0s 303us/step - loss: 0.0246 - accuracy: 0.9841 - val_loss: 0.1419 - val_accuracy: 0.9167\n",
      "Epoch 566/1000\n",
      "63/63 [==============================] - 0s 263us/step - loss: 0.0144 - accuracy: 1.0000 - val_loss: 0.1766 - val_accuracy: 0.8333\n",
      "Epoch 567/1000\n",
      "63/63 [==============================] - 0s 231us/step - loss: 0.0120 - accuracy: 1.0000 - val_loss: 0.1707 - val_accuracy: 0.8333\n",
      "Epoch 568/1000\n",
      "63/63 [==============================] - 0s 271us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 0.1726 - val_accuracy: 0.8333\n",
      "Epoch 569/1000\n",
      "63/63 [==============================] - 0s 242us/step - loss: 0.0240 - accuracy: 1.0000 - val_loss: 0.1707 - val_accuracy: 0.8333\n",
      "Epoch 570/1000\n",
      "63/63 [==============================] - 0s 283us/step - loss: 0.0097 - accuracy: 1.0000 - val_loss: 0.1500 - val_accuracy: 0.9167\n",
      "Epoch 571/1000\n",
      "63/63 [==============================] - 0s 230us/step - loss: 0.0091 - accuracy: 1.0000 - val_loss: 0.1731 - val_accuracy: 0.8333\n",
      "Epoch 572/1000\n",
      "63/63 [==============================] - 0s 375us/step - loss: 0.0355 - accuracy: 0.9841 - val_loss: 0.1091 - val_accuracy: 0.9167\n",
      "Epoch 573/1000\n",
      "63/63 [==============================] - 0s 247us/step - loss: 0.0176 - accuracy: 0.9841 - val_loss: 0.1498 - val_accuracy: 0.9167\n",
      "Epoch 574/1000\n",
      "63/63 [==============================] - 0s 274us/step - loss: 0.0086 - accuracy: 1.0000 - val_loss: 0.1636 - val_accuracy: 0.9167\n",
      "Epoch 575/1000\n",
      "63/63 [==============================] - 0s 224us/step - loss: 0.0318 - accuracy: 0.9841 - val_loss: 0.1048 - val_accuracy: 0.9167\n",
      "Epoch 576/1000\n",
      "63/63 [==============================] - 0s 221us/step - loss: 0.0516 - accuracy: 0.9841 - val_loss: 0.0817 - val_accuracy: 1.0000\n",
      "Epoch 577/1000\n",
      "63/63 [==============================] - 0s 274us/step - loss: 0.0169 - accuracy: 1.0000 - val_loss: 0.1032 - val_accuracy: 0.9167\n",
      "Epoch 578/1000\n",
      "63/63 [==============================] - 0s 255us/step - loss: 0.0181 - accuracy: 0.9841 - val_loss: 0.0813 - val_accuracy: 1.0000\n",
      "Epoch 579/1000\n",
      "63/63 [==============================] - 0s 183us/step - loss: 0.0948 - accuracy: 0.9683 - val_loss: 0.0872 - val_accuracy: 1.0000\n",
      "Epoch 580/1000\n",
      "63/63 [==============================] - 0s 277us/step - loss: 0.0262 - accuracy: 1.0000 - val_loss: 0.1145 - val_accuracy: 0.9167\n",
      "Epoch 581/1000\n",
      "63/63 [==============================] - 0s 255us/step - loss: 0.0092 - accuracy: 1.0000 - val_loss: 0.1359 - val_accuracy: 0.9167\n",
      "Epoch 582/1000\n",
      "63/63 [==============================] - 0s 208us/step - loss: 0.0097 - accuracy: 1.0000 - val_loss: 0.1472 - val_accuracy: 0.9167\n",
      "Epoch 583/1000\n",
      "63/63 [==============================] - 0s 167us/step - loss: 0.0086 - accuracy: 1.0000 - val_loss: 0.1678 - val_accuracy: 0.9167\n",
      "Epoch 584/1000\n",
      "63/63 [==============================] - 0s 238us/step - loss: 0.0177 - accuracy: 1.0000 - val_loss: 0.1412 - val_accuracy: 0.9167\n",
      "Epoch 585/1000\n",
      "63/63 [==============================] - 0s 206us/step - loss: 0.0300 - accuracy: 0.9841 - val_loss: 0.0908 - val_accuracy: 1.0000\n",
      "Epoch 586/1000\n",
      "63/63 [==============================] - 0s 265us/step - loss: 0.0259 - accuracy: 0.9841 - val_loss: 0.1481 - val_accuracy: 0.9167\n",
      "Epoch 587/1000\n",
      "63/63 [==============================] - 0s 183us/step - loss: 0.0249 - accuracy: 0.9841 - val_loss: 0.2004 - val_accuracy: 0.8333\n",
      "Epoch 588/1000\n",
      "63/63 [==============================] - 0s 303us/step - loss: 0.0237 - accuracy: 0.9841 - val_loss: 0.1370 - val_accuracy: 0.9167\n",
      "Epoch 589/1000\n",
      "63/63 [==============================] - 0s 219us/step - loss: 0.0077 - accuracy: 1.0000 - val_loss: 0.1473 - val_accuracy: 0.9167\n",
      "Epoch 590/1000\n",
      "63/63 [==============================] - 0s 268us/step - loss: 0.0463 - accuracy: 0.9683 - val_loss: 0.1643 - val_accuracy: 0.9167\n",
      "Epoch 591/1000\n",
      "63/63 [==============================] - 0s 163us/step - loss: 0.0812 - accuracy: 0.9841 - val_loss: 0.1452 - val_accuracy: 0.9167\n",
      "Epoch 592/1000\n",
      "63/63 [==============================] - 0s 222us/step - loss: 0.0656 - accuracy: 0.9841 - val_loss: 0.1338 - val_accuracy: 0.9167\n",
      "Epoch 593/1000\n",
      "63/63 [==============================] - 0s 193us/step - loss: 0.0163 - accuracy: 1.0000 - val_loss: 0.1761 - val_accuracy: 0.8333\n",
      "Epoch 594/1000\n",
      "63/63 [==============================] - 0s 238us/step - loss: 0.0079 - accuracy: 1.0000 - val_loss: 0.1803 - val_accuracy: 0.8333\n",
      "Epoch 595/1000\n",
      "63/63 [==============================] - 0s 196us/step - loss: 0.0238 - accuracy: 0.9841 - val_loss: 0.1858 - val_accuracy: 0.8333\n",
      "Epoch 596/1000\n",
      "63/63 [==============================] - 0s 294us/step - loss: 0.0130 - accuracy: 1.0000 - val_loss: 0.2085 - val_accuracy: 0.8333\n",
      "Epoch 597/1000\n",
      "63/63 [==============================] - 0s 198us/step - loss: 0.0478 - accuracy: 0.9841 - val_loss: 0.1474 - val_accuracy: 0.9167\n",
      "Epoch 598/1000\n",
      "63/63 [==============================] - 0s 241us/step - loss: 0.0306 - accuracy: 0.9841 - val_loss: 0.1644 - val_accuracy: 0.9167\n",
      "Epoch 599/1000\n",
      "63/63 [==============================] - 0s 271us/step - loss: 0.0815 - accuracy: 0.9683 - val_loss: 0.1632 - val_accuracy: 0.9167\n",
      "Epoch 600/1000\n",
      "63/63 [==============================] - 0s 240us/step - loss: 0.0190 - accuracy: 1.0000 - val_loss: 0.1565 - val_accuracy: 0.9167\n",
      "Epoch 601/1000\n",
      "63/63 [==============================] - 0s 209us/step - loss: 0.1022 - accuracy: 0.9841 - val_loss: 0.1568 - val_accuracy: 0.9167\n",
      "Epoch 602/1000\n",
      "63/63 [==============================] - 0s 343us/step - loss: 0.0245 - accuracy: 0.9841 - val_loss: 0.2065 - val_accuracy: 0.8333\n",
      "Epoch 603/1000\n",
      "63/63 [==============================] - 0s 255us/step - loss: 0.0067 - accuracy: 1.0000 - val_loss: 0.1998 - val_accuracy: 0.8333\n",
      "Epoch 604/1000\n",
      "63/63 [==============================] - 0s 207us/step - loss: 0.0113 - accuracy: 1.0000 - val_loss: 0.1815 - val_accuracy: 0.8333\n",
      "Epoch 605/1000\n",
      "63/63 [==============================] - 0s 202us/step - loss: 0.0506 - accuracy: 0.9683 - val_loss: 0.1010 - val_accuracy: 0.9167\n",
      "Epoch 606/1000\n",
      "63/63 [==============================] - 0s 319us/step - loss: 0.0119 - accuracy: 1.0000 - val_loss: 0.1237 - val_accuracy: 0.9167\n",
      "Epoch 607/1000\n",
      "63/63 [==============================] - 0s 231us/step - loss: 0.0136 - accuracy: 1.0000 - val_loss: 0.1336 - val_accuracy: 0.9167\n",
      "Epoch 608/1000\n",
      "63/63 [==============================] - 0s 272us/step - loss: 0.0150 - accuracy: 1.0000 - val_loss: 0.1358 - val_accuracy: 0.9167\n",
      "Epoch 609/1000\n",
      "63/63 [==============================] - 0s 245us/step - loss: 0.0380 - accuracy: 0.9841 - val_loss: 0.1710 - val_accuracy: 0.8333\n",
      "Epoch 610/1000\n",
      "63/63 [==============================] - 0s 228us/step - loss: 0.0057 - accuracy: 1.0000 - val_loss: 0.1821 - val_accuracy: 0.8333\n",
      "Epoch 611/1000\n",
      "63/63 [==============================] - 0s 165us/step - loss: 0.0213 - accuracy: 0.9841 - val_loss: 0.1535 - val_accuracy: 0.9167\n",
      "Epoch 612/1000\n",
      "63/63 [==============================] - 0s 247us/step - loss: 0.0582 - accuracy: 0.9841 - val_loss: 0.1048 - val_accuracy: 0.9167\n",
      "Epoch 613/1000\n",
      "63/63 [==============================] - 0s 184us/step - loss: 0.0090 - accuracy: 1.0000 - val_loss: 0.1045 - val_accuracy: 0.9167\n",
      "Epoch 614/1000\n",
      "63/63 [==============================] - 0s 294us/step - loss: 0.0272 - accuracy: 0.9841 - val_loss: 0.0807 - val_accuracy: 1.0000\n",
      "Epoch 615/1000\n",
      "63/63 [==============================] - 0s 203us/step - loss: 0.0146 - accuracy: 1.0000 - val_loss: 0.1053 - val_accuracy: 0.9167\n",
      "Epoch 616/1000\n",
      "63/63 [==============================] - 0s 268us/step - loss: 0.0107 - accuracy: 1.0000 - val_loss: 0.1113 - val_accuracy: 0.9167\n",
      "Epoch 617/1000\n",
      "63/63 [==============================] - 0s 181us/step - loss: 0.0197 - accuracy: 1.0000 - val_loss: 0.1511 - val_accuracy: 0.9167\n",
      "Epoch 618/1000\n",
      "63/63 [==============================] - 0s 256us/step - loss: 0.0201 - accuracy: 0.9841 - val_loss: 0.1550 - val_accuracy: 0.9167\n",
      "Epoch 619/1000\n",
      "63/63 [==============================] - 0s 187us/step - loss: 0.0079 - accuracy: 1.0000 - val_loss: 0.1617 - val_accuracy: 0.9167\n",
      "Epoch 620/1000\n",
      "63/63 [==============================] - 0s 213us/step - loss: 0.0302 - accuracy: 0.9841 - val_loss: 0.1800 - val_accuracy: 0.8333\n",
      "Epoch 621/1000\n",
      "63/63 [==============================] - 0s 203us/step - loss: 0.0949 - accuracy: 0.9683 - val_loss: 0.1782 - val_accuracy: 0.8333\n",
      "Epoch 622/1000\n",
      "63/63 [==============================] - 0s 243us/step - loss: 0.0506 - accuracy: 0.9841 - val_loss: 0.1747 - val_accuracy: 0.8333\n",
      "Epoch 623/1000\n",
      "63/63 [==============================] - 0s 297us/step - loss: 0.0265 - accuracy: 0.9841 - val_loss: 0.2050 - val_accuracy: 0.8333\n",
      "Epoch 624/1000\n",
      "63/63 [==============================] - 0s 285us/step - loss: 0.0239 - accuracy: 1.0000 - val_loss: 0.2101 - val_accuracy: 0.8333\n",
      "Epoch 625/1000\n",
      "63/63 [==============================] - 0s 216us/step - loss: 0.0226 - accuracy: 1.0000 - val_loss: 0.1591 - val_accuracy: 0.9167\n",
      "Epoch 626/1000\n",
      "63/63 [==============================] - 0s 231us/step - loss: 0.0106 - accuracy: 1.0000 - val_loss: 0.1508 - val_accuracy: 0.9167\n",
      "Epoch 627/1000\n",
      "63/63 [==============================] - 0s 187us/step - loss: 0.0536 - accuracy: 0.9841 - val_loss: 0.1127 - val_accuracy: 0.9167\n",
      "Epoch 628/1000\n",
      "63/63 [==============================] - 0s 258us/step - loss: 0.0388 - accuracy: 0.9841 - val_loss: 0.0894 - val_accuracy: 1.0000\n",
      "Epoch 629/1000\n",
      "63/63 [==============================] - 0s 216us/step - loss: 0.0099 - accuracy: 1.0000 - val_loss: 0.0967 - val_accuracy: 0.9167\n",
      "Epoch 630/1000\n",
      "63/63 [==============================] - 0s 303us/step - loss: 0.0100 - accuracy: 1.0000 - val_loss: 0.1046 - val_accuracy: 0.9167\n",
      "Epoch 631/1000\n",
      "63/63 [==============================] - 0s 255us/step - loss: 0.0164 - accuracy: 1.0000 - val_loss: 0.0941 - val_accuracy: 1.0000\n",
      "Epoch 632/1000\n",
      "63/63 [==============================] - 0s 262us/step - loss: 0.0085 - accuracy: 1.0000 - val_loss: 0.1020 - val_accuracy: 0.9167\n",
      "Epoch 633/1000\n",
      "63/63 [==============================] - 0s 268us/step - loss: 0.0178 - accuracy: 0.9841 - val_loss: 0.1229 - val_accuracy: 0.9167\n",
      "Epoch 634/1000\n",
      "63/63 [==============================] - 0s 231us/step - loss: 0.0132 - accuracy: 1.0000 - val_loss: 0.1556 - val_accuracy: 0.9167\n",
      "Epoch 635/1000\n",
      "63/63 [==============================] - 0s 260us/step - loss: 0.0220 - accuracy: 0.9841 - val_loss: 0.1251 - val_accuracy: 0.9167\n",
      "Epoch 636/1000\n",
      "63/63 [==============================] - 0s 257us/step - loss: 0.0305 - accuracy: 0.9841 - val_loss: 0.0865 - val_accuracy: 1.0000\n",
      "Epoch 637/1000\n",
      "63/63 [==============================] - 0s 338us/step - loss: 0.0062 - accuracy: 1.0000 - val_loss: 0.0860 - val_accuracy: 1.0000\n",
      "Epoch 638/1000\n",
      "63/63 [==============================] - 0s 203us/step - loss: 0.0062 - accuracy: 1.0000 - val_loss: 0.0968 - val_accuracy: 0.9167\n",
      "Epoch 639/1000\n",
      "63/63 [==============================] - 0s 291us/step - loss: 0.0133 - accuracy: 1.0000 - val_loss: 0.1274 - val_accuracy: 0.9167\n",
      "Epoch 640/1000\n",
      "63/63 [==============================] - 0s 207us/step - loss: 0.0038 - accuracy: 1.0000 - val_loss: 0.1290 - val_accuracy: 0.9167\n",
      "Epoch 641/1000\n",
      "63/63 [==============================] - 0s 335us/step - loss: 0.0145 - accuracy: 1.0000 - val_loss: 0.1258 - val_accuracy: 0.9167\n",
      "Epoch 642/1000\n",
      "63/63 [==============================] - 0s 277us/step - loss: 0.0120 - accuracy: 1.0000 - val_loss: 0.1443 - val_accuracy: 0.9167\n",
      "Epoch 643/1000\n",
      "63/63 [==============================] - 0s 207us/step - loss: 0.0070 - accuracy: 1.0000 - val_loss: 0.1581 - val_accuracy: 0.9167\n",
      "Epoch 644/1000\n",
      "63/63 [==============================] - 0s 287us/step - loss: 0.0075 - accuracy: 1.0000 - val_loss: 0.1539 - val_accuracy: 0.9167\n",
      "Epoch 645/1000\n",
      "63/63 [==============================] - 0s 207us/step - loss: 0.0266 - accuracy: 0.9841 - val_loss: 0.1260 - val_accuracy: 0.9167\n",
      "Epoch 646/1000\n",
      "63/63 [==============================] - 0s 255us/step - loss: 0.0112 - accuracy: 1.0000 - val_loss: 0.1423 - val_accuracy: 0.9167\n",
      "Epoch 647/1000\n",
      "63/63 [==============================] - 0s 217us/step - loss: 0.0280 - accuracy: 0.9841 - val_loss: 0.0930 - val_accuracy: 1.0000\n",
      "Epoch 648/1000\n",
      "63/63 [==============================] - 0s 288us/step - loss: 0.0156 - accuracy: 1.0000 - val_loss: 0.1442 - val_accuracy: 0.9167\n",
      "Epoch 649/1000\n",
      "63/63 [==============================] - 0s 213us/step - loss: 0.0040 - accuracy: 1.0000 - val_loss: 0.1456 - val_accuracy: 0.9167\n",
      "Epoch 650/1000\n",
      "63/63 [==============================] - 0s 237us/step - loss: 0.0231 - accuracy: 0.9841 - val_loss: 0.1330 - val_accuracy: 0.9167\n",
      "Epoch 651/1000\n",
      "63/63 [==============================] - 0s 228us/step - loss: 0.0043 - accuracy: 1.0000 - val_loss: 0.1289 - val_accuracy: 0.9167\n",
      "Epoch 652/1000\n",
      "63/63 [==============================] - 0s 225us/step - loss: 0.0074 - accuracy: 1.0000 - val_loss: 0.1442 - val_accuracy: 0.9167\n",
      "Epoch 653/1000\n",
      "63/63 [==============================] - 0s 181us/step - loss: 0.0346 - accuracy: 0.9841 - val_loss: 0.1816 - val_accuracy: 0.8333\n",
      "Epoch 654/1000\n",
      "63/63 [==============================] - 0s 271us/step - loss: 0.0058 - accuracy: 1.0000 - val_loss: 0.1782 - val_accuracy: 0.8333\n",
      "Epoch 655/1000\n",
      "63/63 [==============================] - 0s 308us/step - loss: 0.0116 - accuracy: 1.0000 - val_loss: 0.2007 - val_accuracy: 0.8333\n",
      "Epoch 656/1000\n",
      "63/63 [==============================] - 0s 256us/step - loss: 0.0072 - accuracy: 1.0000 - val_loss: 0.2111 - val_accuracy: 0.8333\n",
      "Epoch 657/1000\n",
      "63/63 [==============================] - 0s 216us/step - loss: 0.0929 - accuracy: 0.9841 - val_loss: 0.1756 - val_accuracy: 0.8333\n",
      "Epoch 658/1000\n",
      "63/63 [==============================] - 0s 255us/step - loss: 0.0347 - accuracy: 0.9841 - val_loss: 0.1367 - val_accuracy: 0.9167\n",
      "Epoch 659/1000\n",
      "63/63 [==============================] - 0s 194us/step - loss: 0.0199 - accuracy: 1.0000 - val_loss: 0.1716 - val_accuracy: 0.9167\n",
      "Epoch 660/1000\n",
      "63/63 [==============================] - 0s 304us/step - loss: 0.0043 - accuracy: 1.0000 - val_loss: 0.1670 - val_accuracy: 0.9167\n",
      "Epoch 661/1000\n",
      "63/63 [==============================] - 0s 205us/step - loss: 0.1079 - accuracy: 0.9841 - val_loss: 0.1969 - val_accuracy: 0.8333\n",
      "Epoch 662/1000\n",
      "63/63 [==============================] - 0s 265us/step - loss: 0.0087 - accuracy: 1.0000 - val_loss: 0.1915 - val_accuracy: 0.8333\n",
      "Epoch 663/1000\n",
      "63/63 [==============================] - 0s 205us/step - loss: 0.0071 - accuracy: 1.0000 - val_loss: 0.1859 - val_accuracy: 0.8333\n",
      "Epoch 664/1000\n",
      "63/63 [==============================] - 0s 293us/step - loss: 0.0098 - accuracy: 1.0000 - val_loss: 0.1859 - val_accuracy: 0.8333\n",
      "Epoch 665/1000\n",
      "63/63 [==============================] - 0s 189us/step - loss: 0.0086 - accuracy: 1.0000 - val_loss: 0.1902 - val_accuracy: 0.8333\n",
      "Epoch 666/1000\n",
      "63/63 [==============================] - 0s 291us/step - loss: 0.0188 - accuracy: 1.0000 - val_loss: 0.1990 - val_accuracy: 0.8333\n",
      "Epoch 667/1000\n",
      "63/63 [==============================] - 0s 201us/step - loss: 0.0297 - accuracy: 0.9841 - val_loss: 0.1817 - val_accuracy: 0.8333\n",
      "Epoch 668/1000\n",
      "63/63 [==============================] - 0s 259us/step - loss: 0.0397 - accuracy: 0.9841 - val_loss: 0.1632 - val_accuracy: 0.9167\n",
      "Epoch 669/1000\n",
      "63/63 [==============================] - 0s 177us/step - loss: 0.1172 - accuracy: 0.9683 - val_loss: 0.1449 - val_accuracy: 0.9167\n",
      "Epoch 670/1000\n",
      "63/63 [==============================] - 0s 224us/step - loss: 0.0078 - accuracy: 1.0000 - val_loss: 0.1537 - val_accuracy: 0.9167\n",
      "Epoch 671/1000\n",
      "63/63 [==============================] - 0s 231us/step - loss: 0.0325 - accuracy: 0.9841 - val_loss: 0.1184 - val_accuracy: 0.9167\n",
      "Epoch 672/1000\n",
      "63/63 [==============================] - 0s 283us/step - loss: 0.0131 - accuracy: 1.0000 - val_loss: 0.1382 - val_accuracy: 0.9167\n",
      "Epoch 673/1000\n",
      "63/63 [==============================] - 0s 180us/step - loss: 0.0051 - accuracy: 1.0000 - val_loss: 0.1393 - val_accuracy: 0.9167\n",
      "Epoch 674/1000\n",
      "63/63 [==============================] - 0s 265us/step - loss: 0.0085 - accuracy: 1.0000 - val_loss: 0.1368 - val_accuracy: 0.9167\n",
      "Epoch 675/1000\n",
      "63/63 [==============================] - 0s 169us/step - loss: 0.0040 - accuracy: 1.0000 - val_loss: 0.1352 - val_accuracy: 0.9167\n",
      "Epoch 676/1000\n",
      "63/63 [==============================] - 0s 224us/step - loss: 0.0350 - accuracy: 0.9841 - val_loss: 0.0997 - val_accuracy: 0.9167\n",
      "Epoch 677/1000\n",
      "63/63 [==============================] - 0s 208us/step - loss: 0.0084 - accuracy: 1.0000 - val_loss: 0.1035 - val_accuracy: 0.9167\n",
      "Epoch 678/1000\n",
      "63/63 [==============================] - 0s 303us/step - loss: 0.0112 - accuracy: 1.0000 - val_loss: 0.1213 - val_accuracy: 0.9167\n",
      "Epoch 679/1000\n",
      "63/63 [==============================] - 0s 279us/step - loss: 0.0087 - accuracy: 1.0000 - val_loss: 0.1287 - val_accuracy: 0.9167\n",
      "Epoch 680/1000\n",
      "63/63 [==============================] - 0s 248us/step - loss: 0.0244 - accuracy: 0.9841 - val_loss: 0.1437 - val_accuracy: 0.9167\n",
      "Epoch 681/1000\n",
      "63/63 [==============================] - 0s 167us/step - loss: 0.0167 - accuracy: 1.0000 - val_loss: 0.1254 - val_accuracy: 0.9167\n",
      "Epoch 682/1000\n",
      "63/63 [==============================] - 0s 271us/step - loss: 0.0482 - accuracy: 0.9683 - val_loss: 0.1158 - val_accuracy: 0.9167\n",
      "Epoch 683/1000\n",
      "63/63 [==============================] - 0s 173us/step - loss: 0.0743 - accuracy: 0.9683 - val_loss: 0.0714 - val_accuracy: 1.0000\n",
      "Epoch 684/1000\n",
      "63/63 [==============================] - 0s 223us/step - loss: 0.0318 - accuracy: 0.9841 - val_loss: 0.0748 - val_accuracy: 1.0000\n",
      "Epoch 685/1000\n",
      "63/63 [==============================] - 0s 182us/step - loss: 0.0322 - accuracy: 0.9841 - val_loss: 0.1292 - val_accuracy: 0.9167\n",
      "Epoch 686/1000\n",
      "63/63 [==============================] - 0s 363us/step - loss: 0.0139 - accuracy: 1.0000 - val_loss: 0.1688 - val_accuracy: 0.9167\n",
      "Epoch 687/1000\n",
      "63/63 [==============================] - 0s 249us/step - loss: 0.0442 - accuracy: 0.9841 - val_loss: 0.1257 - val_accuracy: 0.9167\n",
      "Epoch 688/1000\n",
      "63/63 [==============================] - 0s 283us/step - loss: 0.0124 - accuracy: 1.0000 - val_loss: 0.1452 - val_accuracy: 0.9167\n",
      "Epoch 689/1000\n",
      "63/63 [==============================] - 0s 195us/step - loss: 0.0139 - accuracy: 1.0000 - val_loss: 0.1995 - val_accuracy: 0.8333\n",
      "Epoch 690/1000\n",
      "63/63 [==============================] - 0s 263us/step - loss: 0.0199 - accuracy: 1.0000 - val_loss: 0.1860 - val_accuracy: 0.8333\n",
      "Epoch 691/1000\n",
      "63/63 [==============================] - 0s 203us/step - loss: 0.0303 - accuracy: 0.9841 - val_loss: 0.1671 - val_accuracy: 0.9167\n",
      "Epoch 692/1000\n",
      "63/63 [==============================] - 0s 199us/step - loss: 0.0043 - accuracy: 1.0000 - val_loss: 0.1772 - val_accuracy: 0.8333\n",
      "Epoch 693/1000\n",
      "63/63 [==============================] - 0s 210us/step - loss: 0.0079 - accuracy: 1.0000 - val_loss: 0.1770 - val_accuracy: 0.8333\n",
      "Epoch 694/1000\n",
      "63/63 [==============================] - 0s 273us/step - loss: 0.0196 - accuracy: 1.0000 - val_loss: 0.1370 - val_accuracy: 0.9167\n",
      "Epoch 695/1000\n",
      "63/63 [==============================] - 0s 231us/step - loss: 0.0044 - accuracy: 1.0000 - val_loss: 0.1422 - val_accuracy: 0.9167\n",
      "Epoch 696/1000\n",
      "63/63 [==============================] - 0s 300us/step - loss: 0.0108 - accuracy: 1.0000 - val_loss: 0.1779 - val_accuracy: 0.8333\n",
      "Epoch 697/1000\n",
      "63/63 [==============================] - 0s 199us/step - loss: 0.0328 - accuracy: 0.9841 - val_loss: 0.1102 - val_accuracy: 0.9167\n",
      "Epoch 698/1000\n",
      "63/63 [==============================] - 0s 280us/step - loss: 0.0146 - accuracy: 1.0000 - val_loss: 0.0842 - val_accuracy: 1.0000\n",
      "Epoch 699/1000\n",
      "63/63 [==============================] - 0s 150us/step - loss: 0.0227 - accuracy: 0.9841 - val_loss: 0.0753 - val_accuracy: 1.0000\n",
      "Epoch 700/1000\n",
      "63/63 [==============================] - 0s 241us/step - loss: 0.0400 - accuracy: 0.9841 - val_loss: 0.0718 - val_accuracy: 1.0000\n",
      "Epoch 701/1000\n",
      "63/63 [==============================] - 0s 215us/step - loss: 0.1138 - accuracy: 0.9841 - val_loss: 0.1009 - val_accuracy: 0.9167\n",
      "Epoch 702/1000\n",
      "63/63 [==============================] - 0s 288us/step - loss: 0.0073 - accuracy: 1.0000 - val_loss: 0.1130 - val_accuracy: 0.9167\n",
      "Epoch 703/1000\n",
      "63/63 [==============================] - 0s 205us/step - loss: 0.0447 - accuracy: 0.9683 - val_loss: 0.1096 - val_accuracy: 0.9167\n",
      "Epoch 704/1000\n",
      "63/63 [==============================] - 0s 249us/step - loss: 0.0129 - accuracy: 1.0000 - val_loss: 0.1213 - val_accuracy: 0.9167\n",
      "Epoch 705/1000\n",
      "63/63 [==============================] - 0s 160us/step - loss: 0.0455 - accuracy: 0.9683 - val_loss: 0.0811 - val_accuracy: 1.0000\n",
      "Epoch 706/1000\n",
      "63/63 [==============================] - 0s 232us/step - loss: 0.0185 - accuracy: 0.9841 - val_loss: 0.0723 - val_accuracy: 1.0000\n",
      "Epoch 707/1000\n",
      "63/63 [==============================] - 0s 237us/step - loss: 0.0093 - accuracy: 1.0000 - val_loss: 0.0723 - val_accuracy: 1.0000\n",
      "Epoch 708/1000\n",
      "63/63 [==============================] - 0s 335us/step - loss: 0.0234 - accuracy: 0.9841 - val_loss: 0.0514 - val_accuracy: 1.0000\n",
      "Epoch 709/1000\n",
      "63/63 [==============================] - 0s 221us/step - loss: 0.0256 - accuracy: 0.9841 - val_loss: 0.0816 - val_accuracy: 1.0000\n",
      "Epoch 710/1000\n",
      "63/63 [==============================] - 0s 222us/step - loss: 0.0468 - accuracy: 0.9841 - val_loss: 0.1400 - val_accuracy: 0.9167\n",
      "Epoch 711/1000\n",
      "63/63 [==============================] - 0s 224us/step - loss: 0.0276 - accuracy: 0.9841 - val_loss: 0.1014 - val_accuracy: 0.9167\n",
      "Epoch 712/1000\n",
      "63/63 [==============================] - 0s 239us/step - loss: 0.0164 - accuracy: 1.0000 - val_loss: 0.1371 - val_accuracy: 0.9167\n",
      "Epoch 713/1000\n",
      "63/63 [==============================] - 0s 227us/step - loss: 0.0055 - accuracy: 1.0000 - val_loss: 0.1426 - val_accuracy: 0.9167\n",
      "Epoch 714/1000\n",
      "63/63 [==============================] - 0s 345us/step - loss: 0.0050 - accuracy: 1.0000 - val_loss: 0.1408 - val_accuracy: 0.9167\n",
      "Epoch 715/1000\n",
      "63/63 [==============================] - 0s 200us/step - loss: 0.0975 - accuracy: 0.9841 - val_loss: 0.1472 - val_accuracy: 0.9167\n",
      "Epoch 716/1000\n",
      "63/63 [==============================] - 0s 283us/step - loss: 0.0967 - accuracy: 0.9683 - val_loss: 0.1100 - val_accuracy: 0.9167\n",
      "Epoch 717/1000\n",
      "63/63 [==============================] - 0s 213us/step - loss: 0.0113 - accuracy: 1.0000 - val_loss: 0.1331 - val_accuracy: 0.9167\n",
      "Epoch 718/1000\n",
      "63/63 [==============================] - 0s 305us/step - loss: 0.0071 - accuracy: 1.0000 - val_loss: 0.1162 - val_accuracy: 0.9167\n",
      "Epoch 719/1000\n",
      "63/63 [==============================] - 0s 200us/step - loss: 0.0259 - accuracy: 1.0000 - val_loss: 0.1018 - val_accuracy: 0.9167\n",
      "Epoch 720/1000\n",
      "63/63 [==============================] - 0s 302us/step - loss: 0.0078 - accuracy: 1.0000 - val_loss: 0.1007 - val_accuracy: 0.9167\n",
      "Epoch 721/1000\n",
      "63/63 [==============================] - 0s 194us/step - loss: 0.0038 - accuracy: 1.0000 - val_loss: 0.1051 - val_accuracy: 0.9167\n",
      "Epoch 722/1000\n",
      "63/63 [==============================] - 0s 277us/step - loss: 0.0185 - accuracy: 1.0000 - val_loss: 0.0966 - val_accuracy: 0.9167\n",
      "Epoch 723/1000\n",
      "63/63 [==============================] - 0s 263us/step - loss: 0.0089 - accuracy: 1.0000 - val_loss: 0.0933 - val_accuracy: 0.9167\n",
      "Epoch 724/1000\n",
      "63/63 [==============================] - 0s 208us/step - loss: 0.0107 - accuracy: 1.0000 - val_loss: 0.1161 - val_accuracy: 0.9167\n",
      "Epoch 725/1000\n",
      "63/63 [==============================] - 0s 201us/step - loss: 0.0087 - accuracy: 1.0000 - val_loss: 0.1302 - val_accuracy: 0.9167\n",
      "Epoch 726/1000\n",
      "63/63 [==============================] - 0s 269us/step - loss: 0.0058 - accuracy: 1.0000 - val_loss: 0.1380 - val_accuracy: 0.9167\n",
      "Epoch 727/1000\n",
      "63/63 [==============================] - 0s 210us/step - loss: 0.0120 - accuracy: 1.0000 - val_loss: 0.1292 - val_accuracy: 0.9167\n",
      "Epoch 728/1000\n",
      "63/63 [==============================] - 0s 284us/step - loss: 0.0910 - accuracy: 0.9841 - val_loss: 0.1577 - val_accuracy: 0.9167\n",
      "Epoch 729/1000\n",
      "63/63 [==============================] - 0s 168us/step - loss: 0.0152 - accuracy: 1.0000 - val_loss: 0.1977 - val_accuracy: 0.8333\n",
      "Epoch 730/1000\n",
      "63/63 [==============================] - 0s 256us/step - loss: 0.0387 - accuracy: 0.9683 - val_loss: 0.1562 - val_accuracy: 0.9167\n",
      "Epoch 731/1000\n",
      "63/63 [==============================] - 0s 200us/step - loss: 0.0178 - accuracy: 1.0000 - val_loss: 0.1472 - val_accuracy: 0.9167\n",
      "Epoch 732/1000\n",
      "63/63 [==============================] - 0s 289us/step - loss: 0.0740 - accuracy: 0.9841 - val_loss: 0.1506 - val_accuracy: 0.9167\n",
      "Epoch 733/1000\n",
      "63/63 [==============================] - 0s 224us/step - loss: 0.0114 - accuracy: 1.0000 - val_loss: 0.1466 - val_accuracy: 0.9167\n",
      "Epoch 734/1000\n",
      "63/63 [==============================] - 0s 212us/step - loss: 0.0092 - accuracy: 1.0000 - val_loss: 0.1536 - val_accuracy: 0.9167\n",
      "Epoch 735/1000\n",
      "63/63 [==============================] - 0s 223us/step - loss: 0.0158 - accuracy: 1.0000 - val_loss: 0.1783 - val_accuracy: 0.8333\n",
      "Epoch 736/1000\n",
      "63/63 [==============================] - 0s 261us/step - loss: 0.0166 - accuracy: 1.0000 - val_loss: 0.1897 - val_accuracy: 0.8333\n",
      "Epoch 737/1000\n",
      "63/63 [==============================] - 0s 247us/step - loss: 0.0125 - accuracy: 1.0000 - val_loss: 0.1622 - val_accuracy: 0.9167\n",
      "Epoch 738/1000\n",
      "63/63 [==============================] - 0s 246us/step - loss: 0.0290 - accuracy: 0.9841 - val_loss: 0.1372 - val_accuracy: 0.9167\n",
      "Epoch 739/1000\n",
      "63/63 [==============================] - 0s 274us/step - loss: 0.0247 - accuracy: 0.9841 - val_loss: 0.0947 - val_accuracy: 0.9167\n",
      "Epoch 740/1000\n",
      "63/63 [==============================] - 0s 238us/step - loss: 0.0072 - accuracy: 1.0000 - val_loss: 0.1094 - val_accuracy: 0.9167\n",
      "Epoch 741/1000\n",
      "63/63 [==============================] - 0s 248us/step - loss: 0.0165 - accuracy: 1.0000 - val_loss: 0.1005 - val_accuracy: 0.9167\n",
      "Epoch 742/1000\n",
      "63/63 [==============================] - 0s 233us/step - loss: 0.0254 - accuracy: 0.9841 - val_loss: 0.1563 - val_accuracy: 0.9167\n",
      "Epoch 743/1000\n",
      "63/63 [==============================] - 0s 239us/step - loss: 0.0107 - accuracy: 1.0000 - val_loss: 0.1797 - val_accuracy: 0.8333\n",
      "Epoch 744/1000\n",
      "63/63 [==============================] - 0s 196us/step - loss: 0.0135 - accuracy: 1.0000 - val_loss: 0.1694 - val_accuracy: 0.9167\n",
      "Epoch 745/1000\n",
      "63/63 [==============================] - 0s 233us/step - loss: 0.0077 - accuracy: 1.0000 - val_loss: 0.1688 - val_accuracy: 0.9167\n",
      "Epoch 746/1000\n",
      "63/63 [==============================] - 0s 230us/step - loss: 0.0065 - accuracy: 1.0000 - val_loss: 0.1786 - val_accuracy: 0.8333\n",
      "Epoch 747/1000\n",
      "63/63 [==============================] - 0s 244us/step - loss: 0.0063 - accuracy: 1.0000 - val_loss: 0.1768 - val_accuracy: 0.8333\n",
      "Epoch 748/1000\n",
      "63/63 [==============================] - 0s 218us/step - loss: 0.0468 - accuracy: 0.9841 - val_loss: 0.1100 - val_accuracy: 0.9167\n",
      "Epoch 749/1000\n",
      "63/63 [==============================] - 0s 227us/step - loss: 0.0050 - accuracy: 1.0000 - val_loss: 0.1150 - val_accuracy: 0.9167\n",
      "Epoch 750/1000\n",
      "63/63 [==============================] - 0s 281us/step - loss: 0.0089 - accuracy: 1.0000 - val_loss: 0.1233 - val_accuracy: 0.9167\n",
      "Epoch 751/1000\n",
      "63/63 [==============================] - 0s 217us/step - loss: 0.0148 - accuracy: 1.0000 - val_loss: 0.1397 - val_accuracy: 0.9167\n",
      "Epoch 752/1000\n",
      "63/63 [==============================] - 0s 205us/step - loss: 0.0051 - accuracy: 1.0000 - val_loss: 0.1448 - val_accuracy: 0.9167\n",
      "Epoch 753/1000\n",
      "63/63 [==============================] - 0s 191us/step - loss: 0.0931 - accuracy: 0.9841 - val_loss: 0.1506 - val_accuracy: 0.9167\n",
      "Epoch 754/1000\n",
      "63/63 [==============================] - 0s 191us/step - loss: 0.0339 - accuracy: 0.9841 - val_loss: 0.1407 - val_accuracy: 0.9167\n",
      "Epoch 755/1000\n",
      "63/63 [==============================] - 0s 201us/step - loss: 0.0527 - accuracy: 0.9841 - val_loss: 0.0986 - val_accuracy: 0.9167\n",
      "Epoch 756/1000\n",
      "63/63 [==============================] - 0s 272us/step - loss: 0.0048 - accuracy: 1.0000 - val_loss: 0.1049 - val_accuracy: 0.9167\n",
      "Epoch 757/1000\n",
      "63/63 [==============================] - 0s 238us/step - loss: 0.0099 - accuracy: 1.0000 - val_loss: 0.1236 - val_accuracy: 0.9167\n",
      "Epoch 758/1000\n",
      "63/63 [==============================] - 0s 233us/step - loss: 0.0149 - accuracy: 1.0000 - val_loss: 0.1665 - val_accuracy: 0.9167\n",
      "Epoch 759/1000\n",
      "63/63 [==============================] - 0s 250us/step - loss: 0.0072 - accuracy: 1.0000 - val_loss: 0.1585 - val_accuracy: 0.9167\n",
      "Epoch 760/1000\n",
      "63/63 [==============================] - 0s 225us/step - loss: 0.0059 - accuracy: 1.0000 - val_loss: 0.1585 - val_accuracy: 0.9167\n",
      "Epoch 761/1000\n",
      "63/63 [==============================] - 0s 239us/step - loss: 0.0371 - accuracy: 0.9683 - val_loss: 0.1997 - val_accuracy: 0.8333\n",
      "Epoch 762/1000\n",
      "63/63 [==============================] - 0s 294us/step - loss: 0.0035 - accuracy: 1.0000 - val_loss: 0.1960 - val_accuracy: 0.8333\n",
      "Epoch 763/1000\n",
      "63/63 [==============================] - 0s 408us/step - loss: 0.0377 - accuracy: 0.9841 - val_loss: 0.1159 - val_accuracy: 0.9167\n",
      "Epoch 764/1000\n",
      "63/63 [==============================] - 0s 297us/step - loss: 0.0174 - accuracy: 1.0000 - val_loss: 0.1103 - val_accuracy: 0.9167\n",
      "Epoch 765/1000\n",
      "63/63 [==============================] - 0s 248us/step - loss: 0.0190 - accuracy: 0.9841 - val_loss: 0.1590 - val_accuracy: 0.9167\n",
      "Epoch 766/1000\n",
      "63/63 [==============================] - 0s 291us/step - loss: 0.0095 - accuracy: 1.0000 - val_loss: 0.1666 - val_accuracy: 0.9167\n",
      "Epoch 767/1000\n",
      "63/63 [==============================] - 0s 224us/step - loss: 0.0408 - accuracy: 0.9841 - val_loss: 0.1589 - val_accuracy: 0.9167\n",
      "Epoch 768/1000\n",
      "63/63 [==============================] - 0s 215us/step - loss: 0.0033 - accuracy: 1.0000 - val_loss: 0.1611 - val_accuracy: 0.9167\n",
      "Epoch 769/1000\n",
      "63/63 [==============================] - 0s 262us/step - loss: 0.0043 - accuracy: 1.0000 - val_loss: 0.1719 - val_accuracy: 0.9167\n",
      "Epoch 770/1000\n",
      "63/63 [==============================] - 0s 282us/step - loss: 0.0173 - accuracy: 0.9841 - val_loss: 0.1398 - val_accuracy: 0.9167\n",
      "Epoch 771/1000\n",
      "63/63 [==============================] - 0s 208us/step - loss: 0.0228 - accuracy: 1.0000 - val_loss: 0.1173 - val_accuracy: 0.9167\n",
      "Epoch 772/1000\n",
      "63/63 [==============================] - 0s 260us/step - loss: 0.0311 - accuracy: 0.9841 - val_loss: 0.1761 - val_accuracy: 0.9167\n",
      "Epoch 773/1000\n",
      "63/63 [==============================] - 0s 227us/step - loss: 0.0081 - accuracy: 1.0000 - val_loss: 0.1803 - val_accuracy: 0.8333\n",
      "Epoch 774/1000\n",
      "63/63 [==============================] - 0s 288us/step - loss: 0.0101 - accuracy: 1.0000 - val_loss: 0.1937 - val_accuracy: 0.8333\n",
      "Epoch 775/1000\n",
      "63/63 [==============================] - 0s 153us/step - loss: 0.0117 - accuracy: 1.0000 - val_loss: 0.2277 - val_accuracy: 0.8333\n",
      "Epoch 776/1000\n",
      "63/63 [==============================] - 0s 255us/step - loss: 0.0068 - accuracy: 1.0000 - val_loss: 0.2153 - val_accuracy: 0.8333\n",
      "Epoch 777/1000\n",
      "63/63 [==============================] - 0s 302us/step - loss: 0.0150 - accuracy: 1.0000 - val_loss: 0.2090 - val_accuracy: 0.8333\n",
      "Epoch 778/1000\n",
      "63/63 [==============================] - 0s 216us/step - loss: 0.0086 - accuracy: 1.0000 - val_loss: 0.1902 - val_accuracy: 0.8333\n",
      "Epoch 779/1000\n",
      "63/63 [==============================] - 0s 216us/step - loss: 0.0339 - accuracy: 0.9841 - val_loss: 0.1488 - val_accuracy: 0.9167\n",
      "Epoch 780/1000\n",
      "63/63 [==============================] - 0s 271us/step - loss: 0.0194 - accuracy: 0.9841 - val_loss: 0.1259 - val_accuracy: 0.9167\n",
      "Epoch 781/1000\n",
      "63/63 [==============================] - 0s 257us/step - loss: 0.0218 - accuracy: 0.9841 - val_loss: 0.1816 - val_accuracy: 0.8333\n",
      "Epoch 782/1000\n",
      "63/63 [==============================] - 0s 289us/step - loss: 0.0200 - accuracy: 0.9841 - val_loss: 0.1435 - val_accuracy: 0.9167\n",
      "Epoch 783/1000\n",
      "63/63 [==============================] - 0s 200us/step - loss: 0.0160 - accuracy: 0.9841 - val_loss: 0.1823 - val_accuracy: 0.8333\n",
      "Epoch 784/1000\n",
      "63/63 [==============================] - 0s 273us/step - loss: 0.0379 - accuracy: 0.9841 - val_loss: 0.1281 - val_accuracy: 0.9167\n",
      "Epoch 785/1000\n",
      "63/63 [==============================] - 0s 207us/step - loss: 0.0138 - accuracy: 1.0000 - val_loss: 0.1342 - val_accuracy: 0.9167\n",
      "Epoch 786/1000\n",
      "63/63 [==============================] - 0s 241us/step - loss: 0.0054 - accuracy: 1.0000 - val_loss: 0.1434 - val_accuracy: 0.9167\n",
      "Epoch 787/1000\n",
      "63/63 [==============================] - 0s 167us/step - loss: 0.0333 - accuracy: 0.9841 - val_loss: 0.1326 - val_accuracy: 0.9167\n",
      "Epoch 788/1000\n",
      "63/63 [==============================] - 0s 257us/step - loss: 0.0270 - accuracy: 0.9841 - val_loss: 0.1130 - val_accuracy: 0.9167\n",
      "Epoch 789/1000\n",
      "63/63 [==============================] - 0s 224us/step - loss: 0.0056 - accuracy: 1.0000 - val_loss: 0.1129 - val_accuracy: 0.9167\n",
      "Epoch 790/1000\n",
      "63/63 [==============================] - 0s 267us/step - loss: 0.0414 - accuracy: 0.9841 - val_loss: 0.0642 - val_accuracy: 1.0000\n",
      "Epoch 791/1000\n",
      "63/63 [==============================] - 0s 161us/step - loss: 0.0317 - accuracy: 0.9841 - val_loss: 0.1192 - val_accuracy: 0.9167\n",
      "Epoch 792/1000\n",
      "63/63 [==============================] - 0s 247us/step - loss: 0.0064 - accuracy: 1.0000 - val_loss: 0.1252 - val_accuracy: 0.9167\n",
      "Epoch 793/1000\n",
      "63/63 [==============================] - 0s 241us/step - loss: 0.0064 - accuracy: 1.0000 - val_loss: 0.1227 - val_accuracy: 0.9167\n",
      "Epoch 794/1000\n",
      "63/63 [==============================] - 0s 323us/step - loss: 0.0057 - accuracy: 1.0000 - val_loss: 0.1258 - val_accuracy: 0.9167\n",
      "Epoch 795/1000\n",
      "63/63 [==============================] - 0s 219us/step - loss: 0.0215 - accuracy: 1.0000 - val_loss: 0.1093 - val_accuracy: 0.9167\n",
      "Epoch 796/1000\n",
      "63/63 [==============================] - 0s 239us/step - loss: 0.0042 - accuracy: 1.0000 - val_loss: 0.1159 - val_accuracy: 0.9167\n",
      "Epoch 797/1000\n",
      "63/63 [==============================] - 0s 235us/step - loss: 0.0109 - accuracy: 1.0000 - val_loss: 0.1211 - val_accuracy: 0.9167\n",
      "Epoch 798/1000\n",
      "63/63 [==============================] - 0s 255us/step - loss: 0.0369 - accuracy: 0.9841 - val_loss: 0.0956 - val_accuracy: 0.9167\n",
      "Epoch 799/1000\n",
      "63/63 [==============================] - 0s 184us/step - loss: 0.0279 - accuracy: 0.9841 - val_loss: 0.0732 - val_accuracy: 1.0000\n",
      "Epoch 800/1000\n",
      "63/63 [==============================] - 0s 249us/step - loss: 0.0577 - accuracy: 0.9841 - val_loss: 0.0642 - val_accuracy: 1.0000\n",
      "Epoch 801/1000\n",
      "63/63 [==============================] - 0s 167us/step - loss: 0.0155 - accuracy: 1.0000 - val_loss: 0.0949 - val_accuracy: 0.9167\n",
      "Epoch 802/1000\n",
      "63/63 [==============================] - 0s 294us/step - loss: 0.0064 - accuracy: 1.0000 - val_loss: 0.1002 - val_accuracy: 0.9167\n",
      "Epoch 803/1000\n",
      "63/63 [==============================] - 0s 199us/step - loss: 0.0099 - accuracy: 1.0000 - val_loss: 0.1160 - val_accuracy: 0.9167\n",
      "Epoch 804/1000\n",
      "63/63 [==============================] - 0s 240us/step - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.1217 - val_accuracy: 0.9167\n",
      "Epoch 805/1000\n",
      "63/63 [==============================] - 0s 182us/step - loss: 0.0062 - accuracy: 1.0000 - val_loss: 0.1355 - val_accuracy: 0.9167\n",
      "Epoch 806/1000\n",
      "63/63 [==============================] - 0s 297us/step - loss: 0.0161 - accuracy: 0.9841 - val_loss: 0.1194 - val_accuracy: 0.9167\n",
      "Epoch 807/1000\n",
      "63/63 [==============================] - 0s 247us/step - loss: 0.0040 - accuracy: 1.0000 - val_loss: 0.1260 - val_accuracy: 0.9167\n",
      "Epoch 808/1000\n",
      "63/63 [==============================] - 0s 232us/step - loss: 0.0178 - accuracy: 0.9841 - val_loss: 0.1830 - val_accuracy: 0.8333\n",
      "Epoch 809/1000\n",
      "63/63 [==============================] - 0s 199us/step - loss: 0.0164 - accuracy: 0.9841 - val_loss: 0.1628 - val_accuracy: 0.9167\n",
      "Epoch 810/1000\n",
      "63/63 [==============================] - 0s 346us/step - loss: 0.0032 - accuracy: 1.0000 - val_loss: 0.1641 - val_accuracy: 0.9167\n",
      "Epoch 811/1000\n",
      "63/63 [==============================] - 0s 199us/step - loss: 0.0165 - accuracy: 1.0000 - val_loss: 0.1167 - val_accuracy: 0.9167\n",
      "Epoch 812/1000\n",
      "63/63 [==============================] - 0s 233us/step - loss: 0.0600 - accuracy: 0.9841 - val_loss: 0.0920 - val_accuracy: 0.9167\n",
      "Epoch 813/1000\n",
      "63/63 [==============================] - 0s 206us/step - loss: 0.0228 - accuracy: 0.9841 - val_loss: 0.1333 - val_accuracy: 0.9167\n",
      "Epoch 814/1000\n",
      "63/63 [==============================] - 0s 248us/step - loss: 0.0259 - accuracy: 0.9841 - val_loss: 0.0880 - val_accuracy: 0.9167\n",
      "Epoch 815/1000\n",
      "63/63 [==============================] - 0s 256us/step - loss: 0.0841 - accuracy: 0.9841 - val_loss: 0.0931 - val_accuracy: 0.9167\n",
      "Epoch 816/1000\n",
      "63/63 [==============================] - 0s 280us/step - loss: 0.0116 - accuracy: 1.0000 - val_loss: 0.1289 - val_accuracy: 0.9167\n",
      "Epoch 817/1000\n",
      "63/63 [==============================] - 0s 255us/step - loss: 0.0116 - accuracy: 1.0000 - val_loss: 0.1271 - val_accuracy: 0.9167\n",
      "Epoch 818/1000\n",
      "63/63 [==============================] - 0s 224us/step - loss: 0.0045 - accuracy: 1.0000 - val_loss: 0.1304 - val_accuracy: 0.9167\n",
      "Epoch 819/1000\n",
      "63/63 [==============================] - 0s 233us/step - loss: 0.0047 - accuracy: 1.0000 - val_loss: 0.1372 - val_accuracy: 0.9167\n",
      "Epoch 820/1000\n",
      "63/63 [==============================] - 0s 223us/step - loss: 0.0048 - accuracy: 1.0000 - val_loss: 0.1462 - val_accuracy: 0.9167\n",
      "Epoch 821/1000\n",
      "63/63 [==============================] - 0s 167us/step - loss: 0.0081 - accuracy: 1.0000 - val_loss: 0.1333 - val_accuracy: 0.9167\n",
      "Epoch 822/1000\n",
      "63/63 [==============================] - 0s 313us/step - loss: 0.0072 - accuracy: 1.0000 - val_loss: 0.1517 - val_accuracy: 0.9167\n",
      "Epoch 823/1000\n",
      "63/63 [==============================] - 0s 268us/step - loss: 0.0305 - accuracy: 0.9683 - val_loss: 0.1465 - val_accuracy: 0.9167\n",
      "Epoch 824/1000\n",
      "63/63 [==============================] - 0s 174us/step - loss: 0.0055 - accuracy: 1.0000 - val_loss: 0.1394 - val_accuracy: 0.9167\n",
      "Epoch 825/1000\n",
      "63/63 [==============================] - 0s 251us/step - loss: 0.0172 - accuracy: 0.9841 - val_loss: 0.1200 - val_accuracy: 0.9167\n",
      "Epoch 826/1000\n",
      "63/63 [==============================] - 0s 199us/step - loss: 0.0098 - accuracy: 1.0000 - val_loss: 0.1108 - val_accuracy: 0.9167\n",
      "Epoch 827/1000\n",
      "63/63 [==============================] - 0s 308us/step - loss: 0.0039 - accuracy: 1.0000 - val_loss: 0.1105 - val_accuracy: 0.9167\n",
      "Epoch 828/1000\n",
      "63/63 [==============================] - 0s 215us/step - loss: 0.0128 - accuracy: 1.0000 - val_loss: 0.1022 - val_accuracy: 0.9167\n",
      "Epoch 829/1000\n",
      "63/63 [==============================] - 0s 184us/step - loss: 0.0413 - accuracy: 0.9683 - val_loss: 0.1009 - val_accuracy: 0.9167\n",
      "Epoch 830/1000\n",
      "63/63 [==============================] - 0s 257us/step - loss: 0.0043 - accuracy: 1.0000 - val_loss: 0.1072 - val_accuracy: 0.9167\n",
      "Epoch 831/1000\n",
      "63/63 [==============================] - 0s 199us/step - loss: 0.1011 - accuracy: 0.9841 - val_loss: 0.1003 - val_accuracy: 0.9167\n",
      "Epoch 832/1000\n",
      "63/63 [==============================] - 0s 350us/step - loss: 0.0329 - accuracy: 0.9841 - val_loss: 0.0653 - val_accuracy: 1.0000\n",
      "Epoch 833/1000\n",
      "63/63 [==============================] - 0s 232us/step - loss: 0.0068 - accuracy: 1.0000 - val_loss: 0.0703 - val_accuracy: 1.0000\n",
      "Epoch 834/1000\n",
      "63/63 [==============================] - 0s 218us/step - loss: 0.0177 - accuracy: 0.9841 - val_loss: 0.0937 - val_accuracy: 0.9167\n",
      "Epoch 835/1000\n",
      "63/63 [==============================] - 0s 306us/step - loss: 0.1081 - accuracy: 0.9841 - val_loss: 0.0644 - val_accuracy: 1.0000\n",
      "Epoch 836/1000\n",
      "63/63 [==============================] - 0s 256us/step - loss: 0.0207 - accuracy: 1.0000 - val_loss: 0.0659 - val_accuracy: 1.0000\n",
      "Epoch 837/1000\n",
      "63/63 [==============================] - 0s 299us/step - loss: 0.0185 - accuracy: 0.9841 - val_loss: 0.1022 - val_accuracy: 0.9167\n",
      "Epoch 838/1000\n",
      "63/63 [==============================] - 0s 215us/step - loss: 0.0140 - accuracy: 1.0000 - val_loss: 0.1097 - val_accuracy: 0.9167\n",
      "Epoch 839/1000\n",
      "63/63 [==============================] - 0s 316us/step - loss: 0.0079 - accuracy: 1.0000 - val_loss: 0.1347 - val_accuracy: 0.9167\n",
      "Epoch 840/1000\n",
      "63/63 [==============================] - 0s 216us/step - loss: 0.0333 - accuracy: 0.9683 - val_loss: 0.1346 - val_accuracy: 0.9167\n",
      "Epoch 841/1000\n",
      "63/63 [==============================] - 0s 304us/step - loss: 0.0217 - accuracy: 0.9841 - val_loss: 0.1775 - val_accuracy: 0.9167\n",
      "Epoch 842/1000\n",
      "63/63 [==============================] - 0s 256us/step - loss: 0.0454 - accuracy: 0.9841 - val_loss: 0.1265 - val_accuracy: 0.9167\n",
      "Epoch 843/1000\n",
      "63/63 [==============================] - 0s 209us/step - loss: 0.0037 - accuracy: 1.0000 - val_loss: 0.1248 - val_accuracy: 0.9167\n",
      "Epoch 844/1000\n",
      "63/63 [==============================] - 0s 206us/step - loss: 0.0056 - accuracy: 1.0000 - val_loss: 0.1259 - val_accuracy: 0.9167\n",
      "Epoch 845/1000\n",
      "63/63 [==============================] - 0s 289us/step - loss: 0.0090 - accuracy: 1.0000 - val_loss: 0.1595 - val_accuracy: 0.9167\n",
      "Epoch 846/1000\n",
      "63/63 [==============================] - 0s 318us/step - loss: 0.0045 - accuracy: 1.0000 - val_loss: 0.1578 - val_accuracy: 0.9167\n",
      "Epoch 847/1000\n",
      "63/63 [==============================] - 0s 272us/step - loss: 0.0169 - accuracy: 0.9841 - val_loss: 0.2040 - val_accuracy: 0.8333\n",
      "Epoch 848/1000\n",
      "63/63 [==============================] - 0s 319us/step - loss: 0.0047 - accuracy: 1.0000 - val_loss: 0.2040 - val_accuracy: 0.8333\n",
      "Epoch 849/1000\n",
      "63/63 [==============================] - 0s 183us/step - loss: 0.0385 - accuracy: 0.9683 - val_loss: 0.1949 - val_accuracy: 0.8333\n",
      "Epoch 850/1000\n",
      "63/63 [==============================] - 0s 263us/step - loss: 0.0220 - accuracy: 0.9841 - val_loss: 0.1571 - val_accuracy: 0.9167\n",
      "Epoch 851/1000\n",
      "63/63 [==============================] - 0s 248us/step - loss: 0.0078 - accuracy: 1.0000 - val_loss: 0.1347 - val_accuracy: 0.9167\n",
      "Epoch 852/1000\n",
      "63/63 [==============================] - 0s 288us/step - loss: 0.1106 - accuracy: 0.9683 - val_loss: 0.1634 - val_accuracy: 0.9167\n",
      "Epoch 853/1000\n",
      "63/63 [==============================] - 0s 223us/step - loss: 0.0393 - accuracy: 0.9841 - val_loss: 0.1060 - val_accuracy: 0.9167\n",
      "Epoch 854/1000\n",
      "63/63 [==============================] - 0s 247us/step - loss: 0.0294 - accuracy: 0.9841 - val_loss: 0.1313 - val_accuracy: 0.9167\n",
      "Epoch 855/1000\n",
      "63/63 [==============================] - 0s 322us/step - loss: 0.0760 - accuracy: 0.9841 - val_loss: 0.1057 - val_accuracy: 0.9167\n",
      "Epoch 856/1000\n",
      "63/63 [==============================] - 0s 521us/step - loss: 0.0404 - accuracy: 0.9683 - val_loss: 0.0968 - val_accuracy: 0.9167\n",
      "Epoch 857/1000\n",
      "63/63 [==============================] - 0s 238us/step - loss: 0.0056 - accuracy: 1.0000 - val_loss: 0.1071 - val_accuracy: 0.9167\n",
      "Epoch 858/1000\n",
      "63/63 [==============================] - 0s 293us/step - loss: 0.0141 - accuracy: 1.0000 - val_loss: 0.1440 - val_accuracy: 0.9167\n",
      "Epoch 859/1000\n",
      "63/63 [==============================] - 0s 191us/step - loss: 0.0291 - accuracy: 0.9841 - val_loss: 0.2158 - val_accuracy: 0.8333\n",
      "Epoch 860/1000\n",
      "63/63 [==============================] - 0s 305us/step - loss: 0.0248 - accuracy: 0.9841 - val_loss: 0.1590 - val_accuracy: 0.9167\n",
      "Epoch 861/1000\n",
      "63/63 [==============================] - 0s 287us/step - loss: 0.0074 - accuracy: 1.0000 - val_loss: 0.1690 - val_accuracy: 0.9167\n",
      "Epoch 862/1000\n",
      "63/63 [==============================] - 0s 335us/step - loss: 0.1084 - accuracy: 0.9841 - val_loss: 0.0902 - val_accuracy: 0.9167\n",
      "Epoch 863/1000\n",
      "63/63 [==============================] - 0s 304us/step - loss: 0.0415 - accuracy: 0.9841 - val_loss: 0.1510 - val_accuracy: 0.9167\n",
      "Epoch 864/1000\n",
      "63/63 [==============================] - 0s 223us/step - loss: 0.0063 - accuracy: 1.0000 - val_loss: 0.1534 - val_accuracy: 0.9167\n",
      "Epoch 865/1000\n",
      "63/63 [==============================] - 0s 225us/step - loss: 0.0044 - accuracy: 1.0000 - val_loss: 0.1571 - val_accuracy: 0.9167\n",
      "Epoch 866/1000\n",
      "63/63 [==============================] - 0s 216us/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.1621 - val_accuracy: 0.9167\n",
      "Epoch 867/1000\n",
      "63/63 [==============================] - 0s 339us/step - loss: 0.0232 - accuracy: 0.9841 - val_loss: 0.1849 - val_accuracy: 0.8333\n",
      "Epoch 868/1000\n",
      "63/63 [==============================] - 0s 259us/step - loss: 0.0138 - accuracy: 1.0000 - val_loss: 0.1683 - val_accuracy: 0.9167\n",
      "Epoch 869/1000\n",
      "63/63 [==============================] - 0s 486us/step - loss: 0.0375 - accuracy: 0.9841 - val_loss: 0.1300 - val_accuracy: 0.9167\n",
      "Epoch 870/1000\n",
      "63/63 [==============================] - 0s 227us/step - loss: 0.0055 - accuracy: 1.0000 - val_loss: 0.1360 - val_accuracy: 0.9167\n",
      "Epoch 871/1000\n",
      "63/63 [==============================] - 0s 239us/step - loss: 0.0314 - accuracy: 0.9841 - val_loss: 0.2092 - val_accuracy: 0.8333\n",
      "Epoch 872/1000\n",
      "63/63 [==============================] - 0s 277us/step - loss: 0.0176 - accuracy: 0.9841 - val_loss: 0.2748 - val_accuracy: 0.8333\n",
      "Epoch 873/1000\n",
      "63/63 [==============================] - 0s 319us/step - loss: 0.0062 - accuracy: 1.0000 - val_loss: 0.2555 - val_accuracy: 0.8333\n",
      "Epoch 874/1000\n",
      "63/63 [==============================] - 0s 206us/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.2517 - val_accuracy: 0.8333\n",
      "Epoch 875/1000\n",
      "63/63 [==============================] - 0s 256us/step - loss: 0.0232 - accuracy: 0.9841 - val_loss: 0.1978 - val_accuracy: 0.8333\n",
      "Epoch 876/1000\n",
      "63/63 [==============================] - 0s 200us/step - loss: 0.0080 - accuracy: 1.0000 - val_loss: 0.2235 - val_accuracy: 0.8333\n",
      "Epoch 877/1000\n",
      "63/63 [==============================] - 0s 272us/step - loss: 0.0278 - accuracy: 0.9841 - val_loss: 0.1828 - val_accuracy: 0.9167\n",
      "Epoch 878/1000\n",
      "63/63 [==============================] - 0s 266us/step - loss: 0.0041 - accuracy: 1.0000 - val_loss: 0.1739 - val_accuracy: 0.9167\n",
      "Epoch 879/1000\n",
      "63/63 [==============================] - 0s 216us/step - loss: 0.0459 - accuracy: 0.9683 - val_loss: 0.1542 - val_accuracy: 0.9167\n",
      "Epoch 880/1000\n",
      "63/63 [==============================] - 0s 209us/step - loss: 0.0209 - accuracy: 1.0000 - val_loss: 0.1673 - val_accuracy: 0.9167\n",
      "Epoch 881/1000\n",
      "63/63 [==============================] - 0s 279us/step - loss: 0.0038 - accuracy: 1.0000 - val_loss: 0.1705 - val_accuracy: 0.9167\n",
      "Epoch 882/1000\n",
      "63/63 [==============================] - 0s 471us/step - loss: 0.0050 - accuracy: 1.0000 - val_loss: 0.1750 - val_accuracy: 0.9167\n",
      "Epoch 883/1000\n",
      "63/63 [==============================] - 0s 183us/step - loss: 0.0953 - accuracy: 0.9683 - val_loss: 0.0987 - val_accuracy: 0.9167\n",
      "Epoch 884/1000\n",
      "63/63 [==============================] - 0s 258us/step - loss: 0.0285 - accuracy: 0.9841 - val_loss: 0.1325 - val_accuracy: 0.9167\n",
      "Epoch 885/1000\n",
      "63/63 [==============================] - 0s 207us/step - loss: 0.0579 - accuracy: 0.9841 - val_loss: 0.0801 - val_accuracy: 1.0000\n",
      "Epoch 886/1000\n",
      "63/63 [==============================] - 0s 240us/step - loss: 0.0693 - accuracy: 0.9683 - val_loss: 0.0987 - val_accuracy: 0.9167\n",
      "Epoch 887/1000\n",
      "63/63 [==============================] - 0s 190us/step - loss: 0.0044 - accuracy: 1.0000 - val_loss: 0.1046 - val_accuracy: 0.9167\n",
      "Epoch 888/1000\n",
      "63/63 [==============================] - 0s 366us/step - loss: 0.0190 - accuracy: 1.0000 - val_loss: 0.0966 - val_accuracy: 0.9167\n",
      "Epoch 889/1000\n",
      "63/63 [==============================] - 0s 215us/step - loss: 0.0125 - accuracy: 1.0000 - val_loss: 0.1199 - val_accuracy: 0.9167\n",
      "Epoch 890/1000\n",
      "63/63 [==============================] - 0s 269us/step - loss: 0.0164 - accuracy: 1.0000 - val_loss: 0.1120 - val_accuracy: 0.9167\n",
      "Epoch 891/1000\n",
      "63/63 [==============================] - 0s 296us/step - loss: 0.0126 - accuracy: 1.0000 - val_loss: 0.1504 - val_accuracy: 0.9167\n",
      "Epoch 892/1000\n",
      "63/63 [==============================] - 0s 225us/step - loss: 0.0213 - accuracy: 0.9841 - val_loss: 0.2135 - val_accuracy: 0.8333\n",
      "Epoch 893/1000\n",
      "63/63 [==============================] - 0s 261us/step - loss: 0.0410 - accuracy: 0.9841 - val_loss: 0.1807 - val_accuracy: 0.9167\n",
      "Epoch 894/1000\n",
      "63/63 [==============================] - 0s 217us/step - loss: 0.0968 - accuracy: 0.9841 - val_loss: 0.1580 - val_accuracy: 0.9167\n",
      "Epoch 895/1000\n",
      "63/63 [==============================] - 0s 344us/step - loss: 0.0109 - accuracy: 1.0000 - val_loss: 0.1793 - val_accuracy: 0.9167\n",
      "Epoch 896/1000\n",
      "63/63 [==============================] - 0s 292us/step - loss: 0.1088 - accuracy: 0.9841 - val_loss: 0.1847 - val_accuracy: 0.8333\n",
      "Epoch 897/1000\n",
      "63/63 [==============================] - 0s 223us/step - loss: 0.0052 - accuracy: 1.0000 - val_loss: 0.1859 - val_accuracy: 0.8333\n",
      "Epoch 898/1000\n",
      "63/63 [==============================] - 0s 234us/step - loss: 0.0119 - accuracy: 1.0000 - val_loss: 0.2260 - val_accuracy: 0.8333\n",
      "Epoch 899/1000\n",
      "63/63 [==============================] - 0s 222us/step - loss: 0.0339 - accuracy: 0.9841 - val_loss: 0.1356 - val_accuracy: 0.9167\n",
      "Epoch 900/1000\n",
      "63/63 [==============================] - 0s 265us/step - loss: 0.0071 - accuracy: 1.0000 - val_loss: 0.1588 - val_accuracy: 0.9167\n",
      "Epoch 901/1000\n",
      "63/63 [==============================] - 0s 208us/step - loss: 0.0162 - accuracy: 1.0000 - val_loss: 0.1765 - val_accuracy: 0.9167\n",
      "Epoch 902/1000\n",
      "63/63 [==============================] - 0s 233us/step - loss: 0.0034 - accuracy: 1.0000 - val_loss: 0.1764 - val_accuracy: 0.9167\n",
      "Epoch 903/1000\n",
      "63/63 [==============================] - 0s 303us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 0.1743 - val_accuracy: 0.9167\n",
      "Epoch 904/1000\n",
      "63/63 [==============================] - 0s 192us/step - loss: 0.0125 - accuracy: 1.0000 - val_loss: 0.2221 - val_accuracy: 0.8333\n",
      "Epoch 905/1000\n",
      "63/63 [==============================] - 0s 249us/step - loss: 0.0062 - accuracy: 1.0000 - val_loss: 0.2311 - val_accuracy: 0.8333\n",
      "Epoch 906/1000\n",
      "63/63 [==============================] - 0s 244us/step - loss: 0.0194 - accuracy: 0.9841 - val_loss: 0.1728 - val_accuracy: 0.9167\n",
      "Epoch 907/1000\n",
      "63/63 [==============================] - 0s 214us/step - loss: 0.0087 - accuracy: 1.0000 - val_loss: 0.1632 - val_accuracy: 0.9167\n",
      "Epoch 908/1000\n",
      "63/63 [==============================] - 0s 285us/step - loss: 0.0273 - accuracy: 0.9841 - val_loss: 0.1225 - val_accuracy: 0.9167\n",
      "Epoch 909/1000\n",
      "63/63 [==============================] - 0s 360us/step - loss: 0.0095 - accuracy: 1.0000 - val_loss: 0.1467 - val_accuracy: 0.9167\n",
      "Epoch 910/1000\n",
      "63/63 [==============================] - 0s 287us/step - loss: 0.0133 - accuracy: 1.0000 - val_loss: 0.1204 - val_accuracy: 0.9167\n",
      "Epoch 911/1000\n",
      "63/63 [==============================] - 0s 226us/step - loss: 0.0122 - accuracy: 1.0000 - val_loss: 0.1487 - val_accuracy: 0.9167\n",
      "Epoch 912/1000\n",
      "63/63 [==============================] - 0s 211us/step - loss: 0.0242 - accuracy: 1.0000 - val_loss: 0.1308 - val_accuracy: 0.9167\n",
      "Epoch 913/1000\n",
      "63/63 [==============================] - 0s 318us/step - loss: 0.0192 - accuracy: 0.9841 - val_loss: 0.1876 - val_accuracy: 0.8333\n",
      "Epoch 914/1000\n",
      "63/63 [==============================] - 0s 247us/step - loss: 0.0119 - accuracy: 1.0000 - val_loss: 0.1549 - val_accuracy: 0.9167\n",
      "Epoch 915/1000\n",
      "63/63 [==============================] - 0s 320us/step - loss: 0.0047 - accuracy: 1.0000 - val_loss: 0.1595 - val_accuracy: 0.9167\n",
      "Epoch 916/1000\n",
      "63/63 [==============================] - 0s 329us/step - loss: 0.0042 - accuracy: 1.0000 - val_loss: 0.1592 - val_accuracy: 0.9167\n",
      "Epoch 917/1000\n",
      "63/63 [==============================] - 0s 289us/step - loss: 0.0178 - accuracy: 0.9841 - val_loss: 0.2057 - val_accuracy: 0.8333\n",
      "Epoch 918/1000\n",
      "63/63 [==============================] - 0s 343us/step - loss: 0.0075 - accuracy: 1.0000 - val_loss: 0.2025 - val_accuracy: 0.8333\n",
      "Epoch 919/1000\n",
      "63/63 [==============================] - 0s 263us/step - loss: 0.0861 - accuracy: 0.9841 - val_loss: 0.1220 - val_accuracy: 0.9167\n",
      "Epoch 920/1000\n",
      "63/63 [==============================] - 0s 247us/step - loss: 0.0048 - accuracy: 1.0000 - val_loss: 0.1199 - val_accuracy: 0.9167\n",
      "Epoch 921/1000\n",
      "63/63 [==============================] - 0s 250us/step - loss: 0.0113 - accuracy: 1.0000 - val_loss: 0.1001 - val_accuracy: 0.9167\n",
      "Epoch 922/1000\n",
      "63/63 [==============================] - 0s 258us/step - loss: 0.0162 - accuracy: 1.0000 - val_loss: 0.0948 - val_accuracy: 0.9167\n",
      "Epoch 923/1000\n",
      "63/63 [==============================] - 0s 277us/step - loss: 0.0152 - accuracy: 1.0000 - val_loss: 0.1139 - val_accuracy: 0.9167\n",
      "Epoch 924/1000\n",
      "63/63 [==============================] - 0s 209us/step - loss: 0.0119 - accuracy: 1.0000 - val_loss: 0.1279 - val_accuracy: 0.9167\n",
      "Epoch 925/1000\n",
      "63/63 [==============================] - 0s 232us/step - loss: 0.0048 - accuracy: 1.0000 - val_loss: 0.1320 - val_accuracy: 0.9167\n",
      "Epoch 926/1000\n",
      "63/63 [==============================] - 0s 224us/step - loss: 0.0069 - accuracy: 1.0000 - val_loss: 0.1302 - val_accuracy: 0.9167\n",
      "Epoch 927/1000\n",
      "63/63 [==============================] - 0s 279us/step - loss: 0.0071 - accuracy: 1.0000 - val_loss: 0.1461 - val_accuracy: 0.9167\n",
      "Epoch 928/1000\n",
      "63/63 [==============================] - 0s 222us/step - loss: 0.0058 - accuracy: 1.0000 - val_loss: 0.1624 - val_accuracy: 0.9167\n",
      "Epoch 929/1000\n",
      "63/63 [==============================] - 0s 256us/step - loss: 0.0054 - accuracy: 1.0000 - val_loss: 0.1743 - val_accuracy: 0.9167\n",
      "Epoch 930/1000\n",
      "63/63 [==============================] - 0s 255us/step - loss: 0.0993 - accuracy: 0.9683 - val_loss: 0.1733 - val_accuracy: 0.9167\n",
      "Epoch 931/1000\n",
      "63/63 [==============================] - 0s 231us/step - loss: 0.0116 - accuracy: 1.0000 - val_loss: 0.1750 - val_accuracy: 0.9167\n",
      "Epoch 932/1000\n",
      "63/63 [==============================] - 0s 250us/step - loss: 0.0034 - accuracy: 1.0000 - val_loss: 0.1717 - val_accuracy: 0.9167\n",
      "Epoch 933/1000\n",
      "63/63 [==============================] - 0s 325us/step - loss: 0.0046 - accuracy: 1.0000 - val_loss: 0.1763 - val_accuracy: 0.9167\n",
      "Epoch 934/1000\n",
      "63/63 [==============================] - 0s 224us/step - loss: 0.0745 - accuracy: 0.9841 - val_loss: 0.1039 - val_accuracy: 0.9167\n",
      "Epoch 935/1000\n",
      "63/63 [==============================] - 0s 239us/step - loss: 0.0063 - accuracy: 1.0000 - val_loss: 0.1016 - val_accuracy: 0.9167\n",
      "Epoch 936/1000\n",
      "63/63 [==============================] - 0s 201us/step - loss: 0.0263 - accuracy: 0.9841 - val_loss: 0.1580 - val_accuracy: 0.9167\n",
      "Epoch 937/1000\n",
      "63/63 [==============================] - 0s 363us/step - loss: 0.0308 - accuracy: 0.9841 - val_loss: 0.1162 - val_accuracy: 0.9167\n",
      "Epoch 938/1000\n",
      "63/63 [==============================] - 0s 351us/step - loss: 0.0189 - accuracy: 0.9841 - val_loss: 0.0926 - val_accuracy: 0.9167\n",
      "Epoch 939/1000\n",
      "63/63 [==============================] - 0s 203us/step - loss: 0.0196 - accuracy: 0.9841 - val_loss: 0.0622 - val_accuracy: 1.0000\n",
      "Epoch 940/1000\n",
      "63/63 [==============================] - 0s 200us/step - loss: 0.0072 - accuracy: 1.0000 - val_loss: 0.0680 - val_accuracy: 1.0000\n",
      "Epoch 941/1000\n",
      "63/63 [==============================] - 0s 309us/step - loss: 0.0049 - accuracy: 1.0000 - val_loss: 0.0736 - val_accuracy: 1.0000\n",
      "Epoch 942/1000\n",
      "63/63 [==============================] - 0s 216us/step - loss: 0.0129 - accuracy: 1.0000 - val_loss: 0.0971 - val_accuracy: 0.9167\n",
      "Epoch 943/1000\n",
      "63/63 [==============================] - 0s 255us/step - loss: 0.0126 - accuracy: 1.0000 - val_loss: 0.1206 - val_accuracy: 0.9167\n",
      "Epoch 944/1000\n",
      "63/63 [==============================] - 0s 208us/step - loss: 0.0298 - accuracy: 0.9841 - val_loss: 0.1303 - val_accuracy: 0.9167\n",
      "Epoch 945/1000\n",
      "63/63 [==============================] - 0s 260us/step - loss: 0.0160 - accuracy: 0.9841 - val_loss: 0.1628 - val_accuracy: 0.9167\n",
      "Epoch 946/1000\n",
      "63/63 [==============================] - 0s 265us/step - loss: 0.0542 - accuracy: 0.9841 - val_loss: 0.1022 - val_accuracy: 0.9167\n",
      "Epoch 947/1000\n",
      "63/63 [==============================] - 0s 370us/step - loss: 0.0109 - accuracy: 1.0000 - val_loss: 0.1044 - val_accuracy: 0.9167\n",
      "Epoch 948/1000\n",
      "63/63 [==============================] - 0s 184us/step - loss: 0.0433 - accuracy: 0.9841 - val_loss: 0.0833 - val_accuracy: 1.0000\n",
      "Epoch 949/1000\n",
      "63/63 [==============================] - 0s 291us/step - loss: 0.0212 - accuracy: 0.9841 - val_loss: 0.1207 - val_accuracy: 0.9167\n",
      "Epoch 950/1000\n",
      "63/63 [==============================] - 0s 229us/step - loss: 0.0124 - accuracy: 1.0000 - val_loss: 0.1490 - val_accuracy: 0.9167\n",
      "Epoch 951/1000\n",
      "63/63 [==============================] - 0s 313us/step - loss: 0.0516 - accuracy: 0.9841 - val_loss: 0.1785 - val_accuracy: 0.9167\n",
      "Epoch 952/1000\n",
      "63/63 [==============================] - 0s 266us/step - loss: 0.0043 - accuracy: 1.0000 - val_loss: 0.1816 - val_accuracy: 0.9167\n",
      "Epoch 953/1000\n",
      "63/63 [==============================] - 0s 192us/step - loss: 0.0069 - accuracy: 1.0000 - val_loss: 0.1841 - val_accuracy: 0.9167\n",
      "Epoch 954/1000\n",
      "63/63 [==============================] - 0s 191us/step - loss: 0.0446 - accuracy: 0.9841 - val_loss: 0.1314 - val_accuracy: 0.9167\n",
      "Epoch 955/1000\n",
      "63/63 [==============================] - 0s 279us/step - loss: 0.0466 - accuracy: 0.9841 - val_loss: 0.1020 - val_accuracy: 0.9167\n",
      "Epoch 956/1000\n",
      "63/63 [==============================] - 0s 238us/step - loss: 0.0076 - accuracy: 1.0000 - val_loss: 0.1008 - val_accuracy: 0.9167\n",
      "Epoch 957/1000\n",
      "63/63 [==============================] - 0s 250us/step - loss: 0.0047 - accuracy: 1.0000 - val_loss: 0.1069 - val_accuracy: 0.9167\n",
      "Epoch 958/1000\n",
      "63/63 [==============================] - 0s 199us/step - loss: 0.0625 - accuracy: 0.9524 - val_loss: 0.1016 - val_accuracy: 0.9167\n",
      "Epoch 959/1000\n",
      "63/63 [==============================] - 0s 288us/step - loss: 0.0081 - accuracy: 1.0000 - val_loss: 0.1093 - val_accuracy: 0.9167\n",
      "Epoch 960/1000\n",
      "63/63 [==============================] - 0s 239us/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 0.1207 - val_accuracy: 0.9167\n",
      "Epoch 961/1000\n",
      "63/63 [==============================] - 0s 253us/step - loss: 0.0193 - accuracy: 0.9841 - val_loss: 0.1664 - val_accuracy: 0.9167\n",
      "Epoch 962/1000\n",
      "63/63 [==============================] - 0s 249us/step - loss: 0.0197 - accuracy: 0.9841 - val_loss: 0.1178 - val_accuracy: 0.9167\n",
      "Epoch 963/1000\n",
      "63/63 [==============================] - 0s 186us/step - loss: 0.0045 - accuracy: 1.0000 - val_loss: 0.1206 - val_accuracy: 0.9167\n",
      "Epoch 964/1000\n",
      "63/63 [==============================] - 0s 214us/step - loss: 0.0066 - accuracy: 1.0000 - val_loss: 0.1298 - val_accuracy: 0.9167\n",
      "Epoch 965/1000\n",
      "63/63 [==============================] - 0s 805us/step - loss: 0.0081 - accuracy: 1.0000 - val_loss: 0.1499 - val_accuracy: 0.9167\n",
      "Epoch 966/1000\n",
      "63/63 [==============================] - 0s 310us/step - loss: 0.0451 - accuracy: 0.9841 - val_loss: 0.1130 - val_accuracy: 0.9167\n",
      "Epoch 967/1000\n",
      "63/63 [==============================] - 0s 220us/step - loss: 0.0047 - accuracy: 1.0000 - val_loss: 0.1187 - val_accuracy: 0.9167\n",
      "Epoch 968/1000\n",
      "63/63 [==============================] - 0s 248us/step - loss: 0.0346 - accuracy: 0.9841 - val_loss: 0.0794 - val_accuracy: 1.0000\n",
      "Epoch 969/1000\n",
      "63/63 [==============================] - 0s 263us/step - loss: 0.0172 - accuracy: 1.0000 - val_loss: 0.1080 - val_accuracy: 0.9167\n",
      "Epoch 970/1000\n",
      "63/63 [==============================] - 0s 283us/step - loss: 0.0065 - accuracy: 1.0000 - val_loss: 0.1151 - val_accuracy: 0.9167\n",
      "Epoch 971/1000\n",
      "63/63 [==============================] - 0s 183us/step - loss: 0.0288 - accuracy: 0.9841 - val_loss: 0.0798 - val_accuracy: 1.0000\n",
      "Epoch 972/1000\n",
      "63/63 [==============================] - 0s 321us/step - loss: 0.0105 - accuracy: 1.0000 - val_loss: 0.0939 - val_accuracy: 0.9167\n",
      "Epoch 973/1000\n",
      "63/63 [==============================] - 0s 199us/step - loss: 0.0097 - accuracy: 1.0000 - val_loss: 0.0930 - val_accuracy: 0.9167\n",
      "Epoch 974/1000\n",
      "63/63 [==============================] - 0s 269us/step - loss: 0.0045 - accuracy: 1.0000 - val_loss: 0.0964 - val_accuracy: 0.9167\n",
      "Epoch 975/1000\n",
      "63/63 [==============================] - 0s 184us/step - loss: 0.0349 - accuracy: 0.9841 - val_loss: 0.0711 - val_accuracy: 1.0000\n",
      "Epoch 976/1000\n",
      "63/63 [==============================] - 0s 241us/step - loss: 0.0075 - accuracy: 1.0000 - val_loss: 0.0759 - val_accuracy: 1.0000\n",
      "Epoch 977/1000\n",
      "63/63 [==============================] - 0s 240us/step - loss: 0.0102 - accuracy: 1.0000 - val_loss: 0.0688 - val_accuracy: 1.0000\n",
      "Epoch 978/1000\n",
      "63/63 [==============================] - 0s 362us/step - loss: 0.0066 - accuracy: 1.0000 - val_loss: 0.0666 - val_accuracy: 1.0000\n",
      "Epoch 979/1000\n",
      "63/63 [==============================] - 0s 281us/step - loss: 0.0118 - accuracy: 1.0000 - val_loss: 0.0774 - val_accuracy: 1.0000\n",
      "Epoch 980/1000\n",
      "63/63 [==============================] - 0s 183us/step - loss: 0.0170 - accuracy: 1.0000 - val_loss: 0.0712 - val_accuracy: 1.0000\n",
      "Epoch 981/1000\n",
      "63/63 [==============================] - 0s 200us/step - loss: 0.0170 - accuracy: 1.0000 - val_loss: 0.0970 - val_accuracy: 0.9167\n",
      "Epoch 982/1000\n",
      "63/63 [==============================] - 0s 287us/step - loss: 0.0036 - accuracy: 1.0000 - val_loss: 0.1025 - val_accuracy: 0.9167\n",
      "Epoch 983/1000\n",
      "63/63 [==============================] - 0s 271us/step - loss: 0.0900 - accuracy: 0.9841 - val_loss: 0.1153 - val_accuracy: 0.9167\n",
      "Epoch 984/1000\n",
      "63/63 [==============================] - 0s 220us/step - loss: 0.0137 - accuracy: 1.0000 - val_loss: 0.1166 - val_accuracy: 0.9167\n",
      "Epoch 985/1000\n",
      "63/63 [==============================] - 0s 290us/step - loss: 0.1136 - accuracy: 0.9683 - val_loss: 0.0819 - val_accuracy: 1.0000\n",
      "Epoch 986/1000\n",
      "63/63 [==============================] - 0s 256us/step - loss: 0.0990 - accuracy: 0.9841 - val_loss: 0.0710 - val_accuracy: 1.0000\n",
      "Epoch 987/1000\n",
      "63/63 [==============================] - 0s 290us/step - loss: 0.0368 - accuracy: 0.9841 - val_loss: 0.0500 - val_accuracy: 1.0000\n",
      "Epoch 988/1000\n",
      "63/63 [==============================] - 0s 526us/step - loss: 0.1140 - accuracy: 0.9683 - val_loss: 0.0356 - val_accuracy: 1.0000\n",
      "Epoch 989/1000\n",
      "63/63 [==============================] - 0s 223us/step - loss: 0.0236 - accuracy: 1.0000 - val_loss: 0.0435 - val_accuracy: 1.0000\n",
      "Epoch 990/1000\n",
      "63/63 [==============================] - 0s 240us/step - loss: 0.0280 - accuracy: 0.9841 - val_loss: 0.0700 - val_accuracy: 1.0000\n",
      "Epoch 991/1000\n",
      "63/63 [==============================] - 0s 208us/step - loss: 0.0059 - accuracy: 1.0000 - val_loss: 0.0756 - val_accuracy: 1.0000\n",
      "Epoch 992/1000\n",
      "63/63 [==============================] - 0s 301us/step - loss: 0.0217 - accuracy: 1.0000 - val_loss: 0.0918 - val_accuracy: 0.9167\n",
      "Epoch 993/1000\n",
      "63/63 [==============================] - 0s 279us/step - loss: 0.0065 - accuracy: 1.0000 - val_loss: 0.0941 - val_accuracy: 0.9167\n",
      "Epoch 994/1000\n",
      "63/63 [==============================] - 0s 255us/step - loss: 0.0124 - accuracy: 1.0000 - val_loss: 0.1196 - val_accuracy: 0.9167\n",
      "Epoch 995/1000\n",
      "63/63 [==============================] - 0s 284us/step - loss: 0.0170 - accuracy: 1.0000 - val_loss: 0.1381 - val_accuracy: 0.9167\n",
      "Epoch 996/1000\n",
      "63/63 [==============================] - 0s 249us/step - loss: 0.0162 - accuracy: 1.0000 - val_loss: 0.1381 - val_accuracy: 0.9167\n",
      "Epoch 997/1000\n",
      "63/63 [==============================] - 0s 287us/step - loss: 0.0063 - accuracy: 1.0000 - val_loss: 0.1390 - val_accuracy: 0.9167\n",
      "Epoch 998/1000\n",
      "63/63 [==============================] - 0s 353us/step - loss: 0.0044 - accuracy: 1.0000 - val_loss: 0.1406 - val_accuracy: 0.9167\n",
      "Epoch 999/1000\n",
      "63/63 [==============================] - 0s 223us/step - loss: 0.0218 - accuracy: 0.9841 - val_loss: 0.0924 - val_accuracy: 0.9167\n",
      "Epoch 1000/1000\n",
      "63/63 [==============================] - 0s 289us/step - loss: 0.0192 - accuracy: 0.9841 - val_loss: 0.1296 - val_accuracy: 0.9167\n"
     ]
    }
   ],
   "source": [
    "# 建立一个2个中间层的3分类的BP神经网络模型\n",
    "model = Sequential() # 层次模型\n",
    "# 输入层，Dense表示BP层，添加激活函数sigmoid\n",
    "model.add(Dense(12,input_dim=4,activation=\"sigmoid\"))\n",
    "# 中间层1，Dense表示BP层\n",
    "model.add(Dense(24,input_dim=24,activation=\"sigmoid\")) \n",
    "model.add(Dropout(0.5))\n",
    "# 中间层2，Dense表示BP层\n",
    "model.add(Dense(24,input_dim=24,activation=\"sigmoid\"))\n",
    "# 输出层，激活函数改为softmax\n",
    "model.add(Dense(3,input_dim=24,activation=\"softmax\"))\n",
    "# 引入SGD、Adgrad\n",
    "sgd=optimizers.SGD(learning_rate=0.1)\n",
    "ada=optimizers.Adagrad(learning_rate=0.1)\n",
    "# 编译模型\n",
    "model.compile(loss='categorical_crossentropy', optimizer=ada,metrics=['accuracy'])\n",
    "# 训练模型1000次\n",
    "his=model.fit(x_train, y_train, epochs= 1000, batch_size = 8,validation_split=0.15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d3229d7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy : 96.00%\n"
     ]
    }
   ],
   "source": [
    "pre = model.predict(x_test)\n",
    "score=model.evaluate(x_test,y_test,verbose=0)\n",
    "score\n",
    "print(\"Test accuracy : %.2f%%\" %(score[1]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8b57741c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x21d0f15d808>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(his.history['loss'])\n",
    "plt.plot(his.history['val_loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ef636150",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "四组数据的分类结果为： [0, 1, 2, 0]\n"
     ]
    }
   ],
   "source": [
    "# 实验三\n",
    "z_input = np.array([[5.0, 3.5, 1.0, 0.5],[5.5, 2.5, 4.0, 1.0],[7.0, 3.0, 6.0, 2.0],[6.6, 2.5, 1.5, 0.2]])\n",
    "# 对数据进行标准化处理\n",
    "ave_z = np.average(z_input,axis=0)\n",
    "std_z = np.std(z_input)\n",
    "z = (z_input-ave_z)/std_z\n",
    "z\n",
    "# 结果预测\n",
    "pre = model.predict(z)\n",
    "pre_list = []\n",
    "for i in range(4):\n",
    "    p = pre[i].argmax()\n",
    "    pre_list.append(p)\n",
    "print(\"四组数据的分类结果为：\",pre_list)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
